Senai Furkan Yılmaz's starred repositories
RFM-ANALYSIS
RFM is a method used for analyzing customer value, creating customer segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries.
-Predicting-Survival-in-Titanic-Disaster
Background : The RMS Titanic sank in the early morning hours of 15 April 1912 in the North Atlantic Ocean, four days into the ship's maiden voyage from Southampton to New York City. The largest ocean liner in service at the time, Titanic had an estimated 2,224 people on board including passengers and crew when she struck an iceberg at around 23:40 (ship's time) on Sunday, 14 April 1912. Her sinking two hours and forty minutes later at 02:20 (ship's time; 05:18 GMT) on Monday, 15 April, resulted in the deaths of 1,502 people, making it one of the deadliest peacetime marine disasters in history. In accordance with existing practice, Titanic's lifeboat system was designed to ferry passengers to nearby rescue vessels, not to hold everyone on board simultaneously; therefore, with the ship sinking rapidly and help still hours away, there was no safe refuge for many of the passengers and crew. Compounding this, poor management of the evacuation meant many boats were launched before they were completely full.
House-Prices
House Prices Analysis Model
udemy-hadoop-buyukveri-egitimi
Udemy A-Z Hadoop ve Büyük Veri Eğitimi kursuna ait kod ve dosyaları içerir.
jupyter-text2code
A proof-of-concept jupyter extension which converts english queries into relevant python code
feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Python-Feature-Engineering-Cookbook
Python Feature Engineering Cookbook, published by Packt
ieee-fraud-detection
Analysis for IEEE Fraud Detection kaggle competition
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
KNN_AlgorithmMachineLearning
Recruitment Bot project with KNN Algorithm (Machine Learning)
data-scientist-roadmap
Toturials coming with the "data science roadmap" picture.
Makine-Ogrenmesi
Makine Öğrenmesi Türkçe Kaynak
turkce-yapay-zeka-kaynaklari
Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
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?