Pinned Repositories
100-Days-Of-ML-Code
100 Days of ML Coding
2021_ThesisNLPReddit
This is the code repository for my Master Thesis. If you have any questions, please contact me on my ESCP Email. guillaume.karklins_marchay@edu.escp.eu
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
A-Comparison-on-Supervised-and-Semi-Supervised-Machine-Learning-Classifiers-for-Diabetes-Prediction
The thesis aims to compare the selected supervised and semi-supervised ML classifiers to predict gestational diabetes (Type-3). The chosen algorithms have been used previously by other researchers for predicting diabetes. The dataset chosen is PIMA Indians Diabetes Dataset (PIDD), consisting of female patients aged 21 and above. The PIDD dataset comprises 768 instances, of which 500 patients are non-diabetic, and the rest are diabetic (the dataset is imbalanced). Different steps such as data cleaning, feature selection, and binning are done on the dataset, which leads to two datasets, namely, non-binned and binned. Parameter tuning is performed while training the algorithms. In addition, oversampling is done on the training set to cope with the imbalanced nature of the dataset. The built models are evaluated using different performance measures. The results of the study showed that the semi-supervised classifier could perform better compare to supervised methods. The non-binned dataset seemed to be more suitable for this problem. The thesis is finely written and structured. In addition, the selected ML techniques are well explained and motivated.
ai-engineer-guide
A Roadmap to Becoming an AI Engineer — From Zero to AI Engineer 👨🏻🚀 🚀
ai-engineer-roadmap
A roadmap describing the required skills, learning resources and sample tools to become an AI Engineer
AI-Engineer-Roadmap-2024
Credit-Card-Fraud-Detection
Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Inspiration Identify fraudulent credit card transactions. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019
google_analytics_customer_revenue_prediction
UCC undergraduate thesis. Predicting revenue per customer based on web traffic data.
Skin-Cancer-Classification-1
CS412 - Machine Learning Course Project
Enuzor's Repositories
Enuzor/GraphEnhancedMachineLearningForFraud
This project is part of a master's thesis studying how supervised learning models for detecting fraud can be improved by incorporating features based on graph theory
Enuzor/Thesis_Customer_Churn
:memo: Diploma on the topic: "Machine learning in the problem of predicting customer churn"
Enuzor/A-Comparison-on-Supervised-and-Semi-Supervised-Machine-Learning-Classifiers-for-Diabetes-Prediction
The thesis aims to compare the selected supervised and semi-supervised ML classifiers to predict gestational diabetes (Type-3). The chosen algorithms have been used previously by other researchers for predicting diabetes. The dataset chosen is PIMA Indians Diabetes Dataset (PIDD), consisting of female patients aged 21 and above. The PIDD dataset comprises 768 instances, of which 500 patients are non-diabetic, and the rest are diabetic (the dataset is imbalanced). Different steps such as data cleaning, feature selection, and binning are done on the dataset, which leads to two datasets, namely, non-binned and binned. Parameter tuning is performed while training the algorithms. In addition, oversampling is done on the training set to cope with the imbalanced nature of the dataset. The built models are evaluated using different performance measures. The results of the study showed that the semi-supervised classifier could perform better compare to supervised methods. The non-binned dataset seemed to be more suitable for this problem. The thesis is finely written and structured. In addition, the selected ML techniques are well explained and motivated.
Enuzor/Thesis-Fraud
Enuzor/Software-Engineering-Data-IT-Careers-RoadMap
Descripting the Fundementals Path for Modern IT jobs
Enuzor/Customer-Purchase-Intent-Prediction
Research thesis and project using advanced analytics tools and implementing Machine Learning algorithms that predict purchase intention of customer in online shopping using customer behavioral analysis. • Developed a data driven framework for predicting whether a customer is going to make a specific purchase in the near future or not and a accuracy of 90% was acheived. • Advisor: Prof.Srinivasan K
Enuzor/RecommenderSystems_thesis
Recommender Systems is a subject that has occupied the business and research world to a great extent. It is a widely used technology based on methods of machine learning and information retrieval. Recommender Systems, starting in 1995, have developed rapidly in terms of the variety of problems they face, the techniques they use and their practical applications. Such implementations can be found on very popular online systems such as Netflix, Amazon, Pandora and many more. The majority of Recommender Systems are based on the Collaborative Filtering technique. The Collaborative Filtering technique is a process of filtering or evaluating items using the opinions of other users and is based on the assumption that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. Such methods have greatly occupied the research world and therefore the amount of techniques and algorithms that have been developed around this field is great. Specifically in this dissertation we conduct our study on Recommender Systems in social networks. A social network is a set of users who, in addition to interacting with objects, also develop interactions with each other. Our study and experiments are carried out in such systems where the source from which we derive information for the provision of recommendations, extends beyond the user ratings to items, to the relationships that users have developed with each other. In addition, we use techniques known as Link Prediction which are suitable for evaluating similarity between users within a graph, in order to enrich our data.
Enuzor/HEALTH-CARE-COST-ANALYSIS---R-Programming
Analysis of Hospital Costs by R Programming
Enuzor/Thesis2021
Twitter Sentiment Analysis
Enuzor/Msc-Thesis-Project
Interest rate prediction using various financial and econmic variables along with sentiment analysis from twitter to find the effects of variables.
Enuzor/pointers-for-software-engineers
A curated list of topics to start learning software engineering
Enuzor/masters-thesis-ml
Repository for my master's thesis in machine learning at UiB
Enuzor/NLP_thesis
Sentiment analysis of Amazon's customer reviews
Enuzor/Credit-Card-Fraud-Detection-using-Machine-Learning-with-Python
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook
Enuzor/Best-ML-Algorithm-For-CyberBullying-Detection
Enuzor/Sentiment_Analysis
Master Thesis UC3M
Enuzor/Classification-of-Online-Retail-Customers-using-Machine-Learning-Techniques
A repository for my thesis of the Master's Degree of Big Data and Data Science titled "Classification of Online Retail Customers using Machine Learning Techniques".
Enuzor/2021_ThesisNLPReddit
This is the code repository for my Master Thesis. If you have any questions, please contact me on my ESCP Email. guillaume.karklins_marchay@edu.escp.eu
Enuzor/MLProject-ChurnPrediction
Enuzor/Prediction-and-Analysis-of-Degree-of-Suicidal-Ideation-in-Online-Content-using-Machine-Learning
his project is an implementation of Thesis - Prediction and Analysis of Degree of Suicidal Ideation in Online Content by Noah C. Jones B.Sc., Morehouse College (2017) Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 20220 This project has been completed as a mini project under Machine Learning Course under the guidance of Dr. Akshay Deepak , Assistant professor , Computer Science & Engineering Department , NIT Patna.
Enuzor/google_analytics_customer_revenue_prediction
UCC undergraduate thesis. Predicting revenue per customer based on web traffic data.
Enuzor/Retail_Churn_Prediction
To predict customer churn by analyzing customer data collected from survey and customer buying behavior pattern
Enuzor/Forecasting-Retail-Sales-Using-Google-Trends-and-Machine-Learning
Author: Feras Al-Basha; Research Director: Yossiri Adulyasak; Research Director: Laurent Charlin; MSc in Global Supply Chain Management - Mémoire/Thesis; HEC Montréal.
Enuzor/Data-Science-Machine-Learning-Project-with-Source-Code
Data Science and Machine Learning projects with source code.
Enuzor/DataAspirant_codes
Complete machine learning model codes
Enuzor/msc-dissertation-final
Enuzor/Credit-Card-Fraud-Detection
Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Inspiration Identify fraudulent credit card transactions. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019
Enuzor/Loan-Sentiment-Analysis
My Willamette University Data Science thesis
Enuzor/Hospital-Cost-Analysis
Hospital Cost Analysis in R
Enuzor/Thesis-code
Detecting fraud and corruption in international development projects using an automated machine learning model