Categorical Data Analysis in Python
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Slides for talk delivered at the Python Pune meetup on 31st Jan 2014. Categorical data is a huge problem many data scientists face. This talk is about how to tame it
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◇不清楚认证流程以及材料该如何准备；
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◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdfWater Industry Process Automation & Control
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.An Approach to Detecting Writing Styles Based on Clustering Techniques
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Water billing management system project report.pdf
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Hierarchical Digital Twin of a Naval Power System
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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一比一原版(UofT毕业证)多伦多大学毕业证成绩单如何办理
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◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Nuclear Power Economics and Structuring 2024
Title: Nuclear Power Economics and Structuring - 2024 Edition
Produced by: World Nuclear Association Published: April 2024
Report No. 2024/001
© 2024 World Nuclear Association.
Registered in England and Wales, company number 01215741
This report reflects the views
of industry experts but does not
necessarily represent those
of World Nuclear Association’s
individual member organizations.
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Categorical Data Analysis in Python
- 1. 1 Categorical Data Analysis in Python By Jaidev Deshpande Data Scientist, DataCulture Analytics twitter.com/jaidevd
- 2. 2 Problem: Who's likely to attend the next meetup? ● Who comes often? ● Men / Women? ● Where do you live? How far from the venue? ● Proficiency with Python (Beginner / Intermediate / Advanced)? ● Area of interest?
- 3. 3 Something like.. Attendees Features Attendance (%) Gender Pincode Proficiency in Python Interest ... attendee_1 80 M 411013 Intermediate Web ... attendee_2 30 F 411040 Advanced Test / Automation ... attendee_3 55 M 411001 Beginners Scientific ... ... ... ... ... ... ... ... ● 1. Numerical features – continuous and quantitative ● 2. Categorical features – discrete and qualitative
- 4. 4 Common Numerical Operations on Data ● Obviously – add, subtract, multiply divide ● Statistical moments ● Operations in vector spaces – Distance measures – Slicing
- 5. 5 Comparison of Operations Numerical Data Addition, subtract, multiply, divide Mean, Variance, Standard Deviation Vector Spaces – the very idea of 'measuring' Categorical Data (Strings, etc) What's the product of two strings? The average pincode of two areas? &%%#&$$*&!!!! At least get some numbers!
- 6. 6 One-hot Encoding ● [Apples, Oranges, Mangoes] ● sklearn.preprocessing.OneHotEncoder ● sklearn.feature_extraction.DictVectorizer [0, 0, 1; 0, 1, 0; 1, 0, 0]
- 7. 7 Original Data Attendees Features Attendance (%) Gender Pincode Proficiency in Python Interest ... attendee_1 80 [0 1] [1 0 0 … 0] [0 1 0] [1 0 0 0 0 0] ... attendee_2 30 [1 0] [0 1 0 … 0] [1 0 0] [0 1 0 0 0 0] ... attendee_3 55 [0 1] [0 0 1 … 0] [0 0 1] [0 0 1 0 0 0] ... ... ... ... ... ... ... ...
- 9. 9 Correspondence Analysis ● Contingency tables (pandas.crosstab) profeciency advanced beginner intermediate gender F 1 0 0 M 0 1 1 ● Different numerical measures ● Perceptual maps
- 10. 10 Correspondence Analysis ● How are proficiencies related w.r.t gender? (Row profiles) ● How are genders related w.r.t proficiency? (Column profiles) – Cosine similarity – Correlation / Covariance ● How are they interrelated? – Weighted chi-squared distance ● Can the dimensionality be reduced? – Singular value decomposition / PCA – sklearn.decomposition.PCA – sklearn.decomposition.TruncatedSVD
- 11. 11 Sample Problem ● Consider the proficiency and interest features from the original problem ● Fake data with 100 observations ● Contingency matrix: automation scientific web advanced 8 1 7 beginner 13 9 35 intermediate 7 1 19
- 12. 12 Results