Pinned Repositories
Association-rule-learning
churn_machine_learning_project
DataSciencePythonMachineLearning
Diabetes_Classficiation_ML
Context This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Content The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Acknowledgements Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press. Inspiration Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?
feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
home_credit
rfm_analysis
yilmazsfrkn's Repositories
yilmazsfrkn/DataSciencePythonMachineLearning
yilmazsfrkn/Association-rule-learning
yilmazsfrkn/Diabetes_Classficiation_ML
Context This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Content The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Acknowledgements Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press. Inspiration Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?
yilmazsfrkn/churn_machine_learning_project
yilmazsfrkn/feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
yilmazsfrkn/home_credit
yilmazsfrkn/rfm_analysis
yilmazsfrkn/automl_service
Deploy AutoML as a service using Flask
yilmazsfrkn/autoscraper
A Smart, Automatic, Fast and Lightweight Web Scraper for Python
yilmazsfrkn/Bootcamp-Projects
yilmazsfrkn/django-rest-framework
Web APIs for Django. 🎸
yilmazsfrkn/dsbootcamp
yilmazsfrkn/dsbootcamp4
yilmazsfrkn/git-lessons
This repo is for git lessons.
yilmazsfrkn/hello_git
yilmazsfrkn/Hitters-Baseball-Data-Salary-Forecast
Baseball Data Description Major League Baseball Data from the 1986 and 1987 seasons. Usage Hitters Format A data frame with 322 observations of major league players on the following 20 variables. AtBat: Number of times at bat in 1986 Hits: Number of hits in 1986 HmRun: Number of home runs in 1986 Runs: Number of runs in 1986 RBI: Number of runs batted in in 1986 Walks: Number of walks in 1986 Years: Number of years in the major leagues CAtBat: Number of times at bat during his career CHits: Number of hits during his career CHmRun: Number of home runs during his career CRuns: Number of runs during his career CRBI: Number of runs batted in during his career CWalks: Number of walks during his career League: A factor with levels A and N indicating player's league at the end of 1986 Division: A factor with levels E and W indicating player's division at the end of 1986 PutOuts: Number of put outs in 1986 Assists: Number of assists in 1986 Errors: Number of errors in 1986 Salary: 1987 annual salary on opening day in thousands of dollars NewLeague: A factor with levels A and N indicating player's league at the beginning of 1987 Source This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. This is part of the data that was used in the 1988 ASA Graphics Section Poster Session. The salary data were originally from Sports Illustrated, April 20, 1987. The 1986 and career statistics were obtained from The 1987 Baseball Encyclopedia Update published by Collier Books, Macmillan Publishing Company, New York.
yilmazsfrkn/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
yilmazsfrkn/official_joke_api
Official Joke API!
yilmazsfrkn/Python-Twitter-module
yilmazsfrkn/selenium
A browser automation framework and ecosystem.
yilmazsfrkn/tweepy
Twitter for Python!
yilmazsfrkn/udemy-apache-spark
Bu repo udemy spark kursları için oluşturulmuştur.
yilmazsfrkn/udemy-hadoop-buyukveri-egitimi
Udemy A-Z Hadoop ve Büyük Veri Eğitimi kursuna ait kod ve dosyaları içerir.
yilmazsfrkn/UDEMY_WEB_SCRAPING
Udemy Web Scraping Eğitim Notları İçin Bu Repo Oluşturulmuştur.