This activity represented the final assignment for the Data Science and Big Data Analysis course within the domain of Natural Language Processing (NLP) at Pavia University. The dataset utilized for this project is accessible through the following link: https://zenodo.org/record/4561253. Professor Antonino Nocera provided guidance throughout the assignment. The collaborative efforts behind the project involve Arnold Fonkou, Vignesh Kumar Kembu, Ashina Nurkoo, and Seyedkourosh Sajjadi.
Plant Disease Detection using Convolution Neural Network (CNN)IRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect plant diseases from images with high accuracy. The researchers trained a CNN model on a dataset of plant leaf images labeled with 38 different disease classes. The CNN was able to automatically extract features from the input images and classify them into the respective disease classes. The proposed system achieved an average accuracy of 92%, demonstrating that neural networks can effectively detect plant diseases even with limited computing resources. The document provides details on how CNNs work, including their typical layers of convolution, max pooling, and fully connected layers, and discusses previous related work applying deep learning to plant disease detection.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
This document describes a simplified Hangman game project created by three students. The game has the computer randomly select a string and ask the user to guess letters, showing incorrect guesses as stars. The game ends when the user correctly guesses the string or makes too many incorrect guesses. References used in coding the project are listed. The scope of the project was to practice skills, provide entertainment, and earn good grades.
Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for be...Kavika Roy
The Corona Virus – COVID-19 outbreak has brought the whole world to a standstill position, with complete lock-down in several countries. Salute! To every health and security professional. Here we will attempt to perform single data analysis with COVID-19 Dataset Using Python. https://www.datatobiz.com/blog/unraveling-the-u-meaning-from-covid-19-dataset-using-python-a-tutorial-for-beginners/
The document discusses data wrangling and manipulation techniques in machine learning. It covers topics like data exploration, data wrangling, data acquisition, and data manipulation in Python. It demonstrates techniques like loading CSV and Excel files, exploring data through dimensionality checks, slicing, and correlation analysis. The objectives are to perform data wrangling and understand its significance, manipulate data in Python using coercion and merging, and explore data using Python.
The document discusses Mongo-Hadoop integration and provides examples of using the Mongo-Hadoop connector to run MapReduce jobs on data stored in MongoDB. It covers loading and writing data to MongoDB from Hadoop, using Java MapReduce, Hadoop Streaming with Python, and analyzing data with Pig and Hive. Examples show processing an email corpus to build a graph of sender-recipient relationships and message counts.
Jupyter Notebooks for machine learning on Kubernetes & OpenShift | DevNation ...Red Hat Developers
In this session you will see how to take your machine model from development to production by watching the steps involved, which include: 1) Developing a ML model crafted via a Jupyter Notebook directly on top of Kubernetes/OpenShift; 2) Publishing that model as a service to be shared with your team or even the world; and 3) Monitoring the RESTful service via Grafana.
Search engines (e.g. Google.com, Yahoo.com, and Bi
ng.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the
necessary skills to build advanced search engines,
small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses
as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data.
Plant Disease Detection using Convolution Neural Network (CNN)IRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect plant diseases from images with high accuracy. The researchers trained a CNN model on a dataset of plant leaf images labeled with 38 different disease classes. The CNN was able to automatically extract features from the input images and classify them into the respective disease classes. The proposed system achieved an average accuracy of 92%, demonstrating that neural networks can effectively detect plant diseases even with limited computing resources. The document provides details on how CNNs work, including their typical layers of convolution, max pooling, and fully connected layers, and discusses previous related work applying deep learning to plant disease detection.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
This document describes a simplified Hangman game project created by three students. The game has the computer randomly select a string and ask the user to guess letters, showing incorrect guesses as stars. The game ends when the user correctly guesses the string or makes too many incorrect guesses. References used in coding the project are listed. The scope of the project was to practice skills, provide entertainment, and earn good grades.
Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for be...Kavika Roy
The Corona Virus – COVID-19 outbreak has brought the whole world to a standstill position, with complete lock-down in several countries. Salute! To every health and security professional. Here we will attempt to perform single data analysis with COVID-19 Dataset Using Python. https://www.datatobiz.com/blog/unraveling-the-u-meaning-from-covid-19-dataset-using-python-a-tutorial-for-beginners/
The document discusses data wrangling and manipulation techniques in machine learning. It covers topics like data exploration, data wrangling, data acquisition, and data manipulation in Python. It demonstrates techniques like loading CSV and Excel files, exploring data through dimensionality checks, slicing, and correlation analysis. The objectives are to perform data wrangling and understand its significance, manipulate data in Python using coercion and merging, and explore data using Python.
The document discusses Mongo-Hadoop integration and provides examples of using the Mongo-Hadoop connector to run MapReduce jobs on data stored in MongoDB. It covers loading and writing data to MongoDB from Hadoop, using Java MapReduce, Hadoop Streaming with Python, and analyzing data with Pig and Hive. Examples show processing an email corpus to build a graph of sender-recipient relationships and message counts.
Jupyter Notebooks for machine learning on Kubernetes & OpenShift | DevNation ...Red Hat Developers
In this session you will see how to take your machine model from development to production by watching the steps involved, which include: 1) Developing a ML model crafted via a Jupyter Notebook directly on top of Kubernetes/OpenShift; 2) Publishing that model as a service to be shared with your team or even the world; and 3) Monitoring the RESTful service via Grafana.
Search engines (e.g. Google.com, Yahoo.com, and Bi
ng.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the
necessary skills to build advanced search engines,
small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses
as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data.
Here are the answers to the Pandas questions:
1. A pandas Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively called index.
2. A DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). It is a DataFrame that contains columns, which may be of different value types (numeric, string, boolean etc.).
3. To create an empty DataFrame:
```python
import pandas as pd
df = pd.DataFrame()
```
4. To fill missing values in a DataFrame, we can use fillna()
This document discusses using naive bayes algorithm for weather prediction. It describes the problem definition, algorithms used for weather prediction including naive bayes, and explains the naive bayes algorithm and implementation. The document implements naive bayes on a weather dataset, calculates accuracy, precision and recall, and also discusses support vector machine and decision tree algorithms for weather prediction comparison.
Introducing new features in Apache Pinot. In this talk, we will go over indexing support in Pinot, recently added text indexing feature, SQL support, and cloud readiness.
The document describes the order of presentation for a group project. Etukudo Andy will introduce the project and order of presentation. Adewumi Ezekiel will present on the numerical data project and contribution. Fajuko Micheal will run the program and discuss contribution. Finally, Afia Kennedy will provide the conclusion and link the project to previous lectures, discussing contribution.
Workshop "Can my .NET application use less CPU / RAM?", Yevhen TatarynovFwdays
In most cases it’s very hard to predict the number of resources needed for your .NET application. But If you spot some abnormal CPU or RAM usage, how to answer the question “Can my application use less?”.
Let’s see samples from real projects, where optimal resource usage by the application became one of the values for the product owner and see how less resource consumption can be.
The workshop will be actual for .NET developers who are interested in optimization of .NET applications, QA engineers who involved performance testing of .NET applications. It also will be interesting to everyone who "suspected" their .NET applications of non-optimal use of resources, but for some reason did not start an investigation.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Educational Objectives After successfully completing this assignmen.pdfrajeshjangid1865
Educational Objectives: After successfully completing this assignment, the student should be
able to accomplish the following:
Use a loop structure to read user input of unknown size through std::cin and store it in an array.
Use conditional branching to selectively perform computational tasks.
Declare (prototype) and define (implement) functions.
Declare and define functions with arguments of various types, including pointers, references,
const pointers, and const references.
Call functions, making appropriate use of the function arguments and their types.
Make decisions as to appropriate function call parameter type, from among: value, reference,
const reference, pointer, and const pointer.
Create, edit, build and run multi-file projects using the Linux/Emacs/Make environment
announced in the course organizer.
Operational Objectives: Create a project that computes the mean and median of a sequence of
integers received via standard input.
Deliverables: Files: stats.h, stats.cpp, main.cpp, makefile, log.txt. Note that these files constitute
a self-contained project.
Assessment Rubric: The following will be used as a guide when assessing the assignment:
Please self-evaluate your work as part of the development process.
Background
Given a finite collection of n numbers:
The mean is the sum of the numbers divided by n, and
The median is the middle value (in case n is odd) or the average of the two middle values (in
case n is even).
Note that to find the median of a collection of data, it is convenient to first sort the data, that is,
put the data in increasing (or non-decreasing) order. Then the median is just the middle datum in
the sorted sequence (or the average of the two middle data, if there are an even number).
One of the more intuitive sort algorithms is called Insertion Sort, which operates on an array
a[0..n-1] of elements. The idea is to \"insert\" the value of a[i] into the sub-array a[0..i-1] at the
largest possible index that results in the expanded sub-array a[0..i] sorted. We insert at the
highest possible index in order not to place the value ahead of any previously inserted elements
with the same value. The subarray a[0..i-1] is assumed to be sorted at the beginning of each
insertion step. The base case consists of a one-element array a[0..0], which is always sorted.
Here is a \"pseudocode\" description of the algorithm:
The inner loop copies all elements in a[0..i-1] up one index until the correct place for t is found.
Then put t in that place.
Procedural Requirements:
Begin a log file named log.txt. This should be an ascii text file in cop3330/proj1 with the
following header:
This file should document all work done by date and time, including all testing and test results.
Create and work within a separate subdirectory cop3330/proj1. Review the COP 3330 rules
found in Introduction/Work Rules.
Copy all of the files from LIB/proj1. These should include:
In addition you should have the script submit.sh in either your .bin or your.
Comparing EDA with classical and Bayesian analysis.pptxPremaGanesh1
This document provides an overview of exploratory data analysis (EDA) techniques and commonly used tools. It discusses classical and Bayesian statistical analysis approaches as well as EDA. Popular Python libraries for EDA include NumPy, Pandas, Matplotlib and Seaborn. NumPy allows working with multidimensional arrays and matrices while Pandas facilitates working with structured data. The document also provides examples of creating arrays and dataframes, loading data from files, and analyzing datasets using these tools.
The document outlines the steps taken to prepare genetic data for analysis in R, including reading in the data, removing underscores, imputing missing values, recoding alleles, and measuring linkage disequilibrium (LD). Key steps include converting the raw data to a matrix without underscores, imputing missing values using codeGeno, and implementing BigLD to partition SNPs into LD blocks and generate a heatmap of LD on chromosome 21. The entire process is timed, taking approximately 11 minutes to complete.
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
PyTorch is an open-source machine learning library for Python. It is primarily developed by Facebook's AI research group. The document discusses setting up PyTorch, including installing necessary packages and configuring development environments. It also provides examples of core PyTorch concepts like tensors, common datasets, and constructing basic neural networks.
Introduction to objects and inputoutput Ahmad Idrees
Java is a computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible.
This document provides an overview of dictionaries and structuring data in Python. It discusses key-value pairs, creating and initializing dictionaries, accessing and modifying dictionary elements, dictionary methods, use cases for dictionaries, other Python data structures like lists and tuples, combining data structures, best practices, and concludes by emphasizing the importance of dictionaries and structured data in Python code.
This document describes the process of calculating weighted page rank on a dataset containing paper citations. It discusses:
1) Converting the semi-structured data into a graph by processing the indexes and references.
2) Calculating in-degree distribution and using it to determine weights based on in-links and out-links for each node.
3) Calculating page rank through an iterative process of updating ranks based on the weighted page rank formula over 10 iterations, and joining the results with the in-degree distribution to find the top 10 most influential papers.
This document provides an overview of running an image classification workload using IBM PowerAI and the MNIST dataset. It discusses deep learning concepts like neural networks and training flows. It then demonstrates how to set up TensorFlow on an IBM PowerAI trial server, load the MNIST dataset, build and train a basic neural network model for image classification, and evaluate the trained model's accuracy on test data.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
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
Here are the answers to the Pandas questions:
1. A pandas Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively called index.
2. A DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). It is a DataFrame that contains columns, which may be of different value types (numeric, string, boolean etc.).
3. To create an empty DataFrame:
```python
import pandas as pd
df = pd.DataFrame()
```
4. To fill missing values in a DataFrame, we can use fillna()
This document discusses using naive bayes algorithm for weather prediction. It describes the problem definition, algorithms used for weather prediction including naive bayes, and explains the naive bayes algorithm and implementation. The document implements naive bayes on a weather dataset, calculates accuracy, precision and recall, and also discusses support vector machine and decision tree algorithms for weather prediction comparison.
Introducing new features in Apache Pinot. In this talk, we will go over indexing support in Pinot, recently added text indexing feature, SQL support, and cloud readiness.
The document describes the order of presentation for a group project. Etukudo Andy will introduce the project and order of presentation. Adewumi Ezekiel will present on the numerical data project and contribution. Fajuko Micheal will run the program and discuss contribution. Finally, Afia Kennedy will provide the conclusion and link the project to previous lectures, discussing contribution.
Workshop "Can my .NET application use less CPU / RAM?", Yevhen TatarynovFwdays
In most cases it’s very hard to predict the number of resources needed for your .NET application. But If you spot some abnormal CPU or RAM usage, how to answer the question “Can my application use less?”.
Let’s see samples from real projects, where optimal resource usage by the application became one of the values for the product owner and see how less resource consumption can be.
The workshop will be actual for .NET developers who are interested in optimization of .NET applications, QA engineers who involved performance testing of .NET applications. It also will be interesting to everyone who "suspected" their .NET applications of non-optimal use of resources, but for some reason did not start an investigation.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Educational Objectives After successfully completing this assignmen.pdfrajeshjangid1865
Educational Objectives: After successfully completing this assignment, the student should be
able to accomplish the following:
Use a loop structure to read user input of unknown size through std::cin and store it in an array.
Use conditional branching to selectively perform computational tasks.
Declare (prototype) and define (implement) functions.
Declare and define functions with arguments of various types, including pointers, references,
const pointers, and const references.
Call functions, making appropriate use of the function arguments and their types.
Make decisions as to appropriate function call parameter type, from among: value, reference,
const reference, pointer, and const pointer.
Create, edit, build and run multi-file projects using the Linux/Emacs/Make environment
announced in the course organizer.
Operational Objectives: Create a project that computes the mean and median of a sequence of
integers received via standard input.
Deliverables: Files: stats.h, stats.cpp, main.cpp, makefile, log.txt. Note that these files constitute
a self-contained project.
Assessment Rubric: The following will be used as a guide when assessing the assignment:
Please self-evaluate your work as part of the development process.
Background
Given a finite collection of n numbers:
The mean is the sum of the numbers divided by n, and
The median is the middle value (in case n is odd) or the average of the two middle values (in
case n is even).
Note that to find the median of a collection of data, it is convenient to first sort the data, that is,
put the data in increasing (or non-decreasing) order. Then the median is just the middle datum in
the sorted sequence (or the average of the two middle data, if there are an even number).
One of the more intuitive sort algorithms is called Insertion Sort, which operates on an array
a[0..n-1] of elements. The idea is to \"insert\" the value of a[i] into the sub-array a[0..i-1] at the
largest possible index that results in the expanded sub-array a[0..i] sorted. We insert at the
highest possible index in order not to place the value ahead of any previously inserted elements
with the same value. The subarray a[0..i-1] is assumed to be sorted at the beginning of each
insertion step. The base case consists of a one-element array a[0..0], which is always sorted.
Here is a \"pseudocode\" description of the algorithm:
The inner loop copies all elements in a[0..i-1] up one index until the correct place for t is found.
Then put t in that place.
Procedural Requirements:
Begin a log file named log.txt. This should be an ascii text file in cop3330/proj1 with the
following header:
This file should document all work done by date and time, including all testing and test results.
Create and work within a separate subdirectory cop3330/proj1. Review the COP 3330 rules
found in Introduction/Work Rules.
Copy all of the files from LIB/proj1. These should include:
In addition you should have the script submit.sh in either your .bin or your.
Comparing EDA with classical and Bayesian analysis.pptxPremaGanesh1
This document provides an overview of exploratory data analysis (EDA) techniques and commonly used tools. It discusses classical and Bayesian statistical analysis approaches as well as EDA. Popular Python libraries for EDA include NumPy, Pandas, Matplotlib and Seaborn. NumPy allows working with multidimensional arrays and matrices while Pandas facilitates working with structured data. The document also provides examples of creating arrays and dataframes, loading data from files, and analyzing datasets using these tools.
The document outlines the steps taken to prepare genetic data for analysis in R, including reading in the data, removing underscores, imputing missing values, recoding alleles, and measuring linkage disequilibrium (LD). Key steps include converting the raw data to a matrix without underscores, imputing missing values using codeGeno, and implementing BigLD to partition SNPs into LD blocks and generate a heatmap of LD on chromosome 21. The entire process is timed, taking approximately 11 minutes to complete.
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
PyTorch is an open-source machine learning library for Python. It is primarily developed by Facebook's AI research group. The document discusses setting up PyTorch, including installing necessary packages and configuring development environments. It also provides examples of core PyTorch concepts like tensors, common datasets, and constructing basic neural networks.
Introduction to objects and inputoutput Ahmad Idrees
Java is a computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible.
This document provides an overview of dictionaries and structuring data in Python. It discusses key-value pairs, creating and initializing dictionaries, accessing and modifying dictionary elements, dictionary methods, use cases for dictionaries, other Python data structures like lists and tuples, combining data structures, best practices, and concludes by emphasizing the importance of dictionaries and structured data in Python code.
This document describes the process of calculating weighted page rank on a dataset containing paper citations. It discusses:
1) Converting the semi-structured data into a graph by processing the indexes and references.
2) Calculating in-degree distribution and using it to determine weights based on in-links and out-links for each node.
3) Calculating page rank through an iterative process of updating ranks based on the weighted page rank formula over 10 iterations, and joining the results with the in-degree distribution to find the top 10 most influential papers.
This document provides an overview of running an image classification workload using IBM PowerAI and the MNIST dataset. It discusses deep learning concepts like neural networks and training flows. It then demonstrates how to set up TensorFlow on an IBM PowerAI trial server, load the MNIST dataset, build and train a basic neural network model for image classification, and evaluate the trained model's accuracy on test data.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
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
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!
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
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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.
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Intelligence supported media monitoring in veterinary medicine
Fake News and Their Detection
1. Fake News and Their Detection
Data Science and Big Data Analysis
Professor: Antonino Nocera
Team Name: 4V’s
Group members:
Arnold Fonkou
Vignesh Kumar Kembu
Ashina Nurkoo
Seyedkourosh Sajjadi
2. WELFake
Fake News Detection (WELFake) dataset
of 72,134 news articles with 35,028 real
and 37,106 fake news.
This dataset is a part of an ongoing
research on "Fake News Prediction on
Social Media Website" as a doctoral
degree program of Mr. Pawan Kumar
Verma and is partially supported by the
ARTICONF project funded by the
European Union’s Horizon 2020 research
and innovation program.
Columns:
- Serial number (starting from 0)
- Title (about the text news heading)
Text (about the news content)
- Label (0 = fake and 1 = real)
4. Ingestion
From CSV to JSON
Data Conversion
we have converted the
file into JSON to be
closer to reality.
Reading Data
Using PySpark
We used the
DATAFRAME client of
SPARK to read our big
data.
Saving to Hadoop
Write into Hadoop
We read from the data
frame and then we write
it to Hadoop.
5. Reading Section
Import findspark
findspark.init()
import pyspark
from pyspark.sql import *
spark = SparkSession.builder
.master("local[1]")
.appName("PySpark Read JSON")
.getOrCreate()
# Reading multiline json file
multiline_dataframe = spark.read.option("multiline","true")
.json("project_data_sample.json")
multiline_dataframe.head()
Saving Section
multiline_dataframe.write.save('/usr/local/hadoop/user3/dsba1.json',
format='json')
And the data is shown as below:
sqlContext = SQLContext(spark)
df = sqlContext.read.format('json').load('/usr/local/hadoop/user3/dsba1.json')
df.show()
7. Mapper (BoW Creation)
Read Lines
Input Data
The data is given as input
lines to the mapper.
Extract Text
Title and Text Extraction
After reading each line as
a JSON object, we
extract the title and the
text related to that piece
of news from it.
Tokenize
Word Extraction
We perform some data
cleaning and then we
extract every single word
from it.
8. Text Cleaning
import sys
import re
import json
def clean_text(text):
text =
re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*(),]|(
?:%[0-9a-fA-F][0-9a-fA-F]))+', '', text)
text = re.sub(r'[^a-zA-Zs]+', '', text)
return text
Tokenizing
def tokenize(text):
if not isinstance(text, str):
text = str(text)
text = clean_text(text)
text = str.lower(text)
return text.split()
Execution
for line in sys.stdin:
line = line.strip()
Try:
json_obj = json.loads(line)
except:
continue
title = json_obj.get("title", "")
text = json_obj.get("text", "")
title_words = tokenize(title)
text_words = tokenize(text)
for word in title_words + text_words:
print(f"{word}t1")
9. Reducer
Read Lines
Input Data
The data is given as input
lines each containing 2
elements.
Initialize Counter
Word and Count
Extraction
After reading each line a
JON object, we extract
the title and the text
related to that piece of
news from it.
Create BoW
Dictionary
Create a dictionary and
add each word as the key
and its associated count
value as the value.
10. Counter Initialization
import sys
from collections import Counter
import json
bag_of_words = Counter()
Execution
for line in sys.stdin:
line = line.strip()
try:
word, count = line.split("t")
except:
continue
count = int(count)
bag_of_words[word] += count
with open('bow_data.json', 'w') as f:
json.dump(bag_of_words, f)
11. Moving to MongoDb
import json
from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017')
db = client['bow']
collection = db['bow_collection']
with open('bow_data.json', 'r') as f:
bow_data = json.load(f)
collection.insert_one(bow_data)
Performing MapReduce Operation
In the Terminal:
cat db.json | python3 bow_mapper.py | sort | python3 bow_reducer.py
12. HDFS
In the case of dealing with big data, we
could partition our dataset into a number
of batches instead of saving it in a single
file.
Instead of:
multiline_dataframe.write.save('/usr/local/hadoop/user3/dsba1.j
son', format='json')
Use:
partitioned_df = multiline_dataframe.repartition(4, "Unnamed: 0")
partitioned_df.write.save('/usr/local/hadoop/user3/dsba1.json',
format='json')
partition_counts = partitioned_df.rdd.mapPartitions(lambda it:
[sum(1 for _ in it)]).collect()
print(partition_counts)
[482, 480, 519, 519]
13. Create
Database
Create a database for
containing the data.
Import From
Hadoop
Import the JSON File
from Hadoop via
PySpark.
View Data &
Backup
View the data and if it is
inserted correctly then
create a backup before
starting the modifications.
Clean Data
Remove
non-alphanumeric
characters.
Display
Modified Data
Display the modified
content to view
changes.
MongoDB
14. Creating Database
Use an existing database or create a new one:
>use dsdb_dev
Viewing Data
>use dsdb_dev
>show collections
>db.fake_real_news.find()
>db.fake_real_news.aggregate([{$group : {_id: "$label", rest_number
: {$sum : 1}}}])
Creating a Copy
In the Terminal:
mongodump --db dsdb_dev --collection fake_real_news --out
/home/ds/Documents/
Importing From Hadoop
In the Terminal:
mongoimport --db dsdb_dev --collection fake_real_news --file
/usr/local/hadoop/user3/dsba1.json/part-00000-d1623440-4fde-4b
72-b87d-5943bec596d3-c000.json
15. Importing from Hadoop Using PySpark
with open('sampled_data.json', 'w') as file: for line in json_data: file.write(line +
'n')
import json
with open('sampled_data.json') as file:
data = file.readlines()
collection.insert_many([json.loads(line) for line in data])
df =
spark.read.json("/usr/local/hadoop/user3/dsba1.json/part-00000-d16234
40-4fde-4b72-b87d-5943bec596d3-c000.json")
sampled_df = df.sample(fraction=0.8, seed=42)
from pymongo import MongoClient
conn = MongoClient()
db = conn.dsdb_dev
collection = db['sampled_data']
json_data = sampled_df.toJSON().collect()
16. Data Cleaning
>db.fake_real_news.aggregate([
{‘$project': {‘_id': 1, 'Unnamed: 0': 1, 'label': 1, 'text': 1, 'title': 1} }
]). forEach(function(doc) {
if (doc.title) {
var newTitle = doc.title.replace(/[^a-zA-Z0-9 ]/g, '');
db.fake_real_news.update({ '_id': doc._id }, { '$set': { 'title': newTitle } });
}
});
Modified Content Display
>db.fake_real_news.aggregate([
{‘$project': {‘_id': 1, 'Unnamed: 0': 1, 'label': 1, 'text': 1, 'title': 1} }
]);
The file is now ready for word occurrence counting,
which can be done using Jupyter Notebook and
PyMongo.
Backup Restoration
In case of any need, restore the initial file:
>db.fake_real_news.drop()
mongorestore --db dsdb_dev --collection fake_real_news
/home/ds/Documents/dsdb_dev/fake_real_news.bson
17. Count the Number of Words
db.fake_real_news.aggregate ([
{
'$match': {
'label': "0" # the condition for the 'label'
field to be 1
}
},
{
'$project': {
'words': {'$split': [{'$toLower': '$title'}, ' ']} #
Split the lowercase version of the title field into
an array of words
}
},
{
'$unwind': '$words' # Separate documents
for each word
},
{
'$group': {
'_id': {
'word': '$words', # Group by word field
and count
},
'count': {'$sum': 1}
}
},
{
'$project': {
# Project to return only word field, count,
and id
'word': '$_id.word',
'count': 1
}
},
{
'$match': {
'word': {'$ne': None}, # Exclude null or
non-existent values
}
},
{
'$match': {
'$expr': {'$ne': ['$word', '']} # Exclude
empty strings
}
},
{
'$sort': {'count':-1}
}
])
18. Hypotheses
H1
Generation of fake news shall be with
the help of stop words.
Metrics - Average number of stop
words in title shall be higher in fake
news.
H2
Real news shall be short and crisp in
order to generate easy value.
Metrics - Length of the fake news shall
be more than the real ones.
19. H1
We used NLTK to extract stop words from
the title column and compared the
averages between fake and real titles.
The hypothesis is false, as shown by the
figure: fake news (0) is less frequent than
real news (1).
20. H2
The hypothesis is true, as shown by the figures:
fake news (0) tends to be longer than real news
(1).
21. Insights on Data &
Pre-processing
To gain quick insights from the data, we
used word clouds for the titles overall and
for fake/real data.
24. Null Values
The title column contains some null
values, which may cause issues in data
analysis or processing.
We need to fill the null values in the title
column to ensure accurate data analysis.
25. Text Normalization
To further prepare the data, we applied text normalization techniques, including converting
the title and text to lowercase and removing punctuation marks.
26. Classification Model
For the binary classification of the News, we have choose
Random Forest Classifier
Splitting of data in x and y variable and Test and train split of
the data has been performed with 77 & 33 size.
The bag of words has been performed to the text of the news
(X_train & X_test) and by removing the stop words in English.
The Label Y_train & Y_test has the class of the news (Fake = 0
& Real = 1 )
Now the train data is feed to the RandomForestClassifier with
500 trees and the model has been tested with the test data
and the model classification confusion matrix is below.