hgatasagun

hgatasagun's Stars

• murattkiran/food-classification

  Serverless Deep Learning using TensorFlow-Lite and AWS Lambda

  Language:Jupyter Notebook41

• huggingface/candle

  Minimalist ML framework for Rust

  Language:Rust15.8k952

• murattkiran/iris_flask

  Language:Python1

• EmanAbdelWhab/number-plate-recognition-yolov8

  Language:Jupyter Notebook3411

• murattkiran/G2M-insight-for-Cab-Investment-firm

  Language:Jupyter Notebook1

• murattkiran/salary_prediction_with_ml

  Language:Jupyter Notebook1

• aytekincn/SQL-Project-Job-Adverts

  SQL Job Adverts

  2

• murattkiran/Store-Item_Demand_Forecasting_with_LGBM

  Language:Jupyter Notebook1

• murattkiran/mlzoomcamp

  Language:Jupyter Notebook1

• murattkiran/Telco_Churn_Prediction

  Language:Jupyter Notebook2

• sadicesur/telco-customer-churn

  Language:Jupyter Notebook3

• ozzzgur/scalers

  Language:Python2

• murattkiran/ARMUT_ARL_RECOMMENDER_SYSTEM

  Language:Python4

• Birsenn/FeatureEngineering

  Language:Python3

• esratakak/NYC_AirBnb_Data_Analysis

  Language:Jupyter Notebook3

• ozzzgur/pool

  In this post, I will investigate whether there is a connection between drownings in America related to nuclear power plant energy production. The data used during this research were taken from the US Centers for Disease Control & Prevention and Our World in Data, and I aimed to give an introductory information on data manipulation

  Language:Python2

• youssefHosni/Data-Science-Interview-Questions-Answers

  Curated list of data science interview questions and answers

  3.4k777

• ozzzgur/hybrid_recommender

  In this project, we aim to build a hybrid recommender system using both item-based and user-based collaborative filtering methods to make personalized movie recommendations for a given user

  Language:Jupyter Notebook2

• murattkiran/AB_Testing_Project

  Language:Python4

• anilsanli/Sentiment-Analysis-with-Tweets-about-ChatGPT

  Sentiment analysis with 100,000 tweets about chatgpt

  Language:Jupyter Notebook3

• Birsenn/Amazon_RatingProductAndSortingReviews

  Language:Python1

• rojdatay/hybrid_movie_recommendation_system

  Language:Jupyter Notebook2

• Birsenn/AB_Testing

  Language:Python2

• youssefHosni/Data-Science-Interview-Preperation-Resources

  Resoruce to help you to prepare for your comming data science interviews

  1k224

• ozzzgur/scrapy-wordcloud

  In this post, using python’s “scrapy” and “wordcloud” libraries, I will first save the audience reviews of the top 9 movies to a json file and then visualize these comments with word cloud.

  1

• ozzzgur/A-B_Testing

  An A/B test compares Maximum Bidding and Average Bidding methods to determine conversion rates on bombabomba.com. Analyzing the "Purchase" metric, the project offers insights and recommendations based on the statistical analysis of the test results.

  Language:Python1

• murattkiran/Rating_Product_Sorting_Reviews_in_Amazon

  Language:Python2

• ozzzgur/rating_product

  Accurately calculate product ratings based on reviews and sort them effectively. Enhance customer satisfaction, boost sales, and ensure trustworthy shopping experiences on e-commerce platforms like Amazon. Analyze an Amazon dataset, calculate weighted average ratings, and determine the top 20 reviews using the Wilson Lower Bound score.

  Language:Python1

• sadicesur/A-B-Testing

  A/B Testing

  Language:Jupyter Notebook2

• ozzzgur/titanic_probability_game

  By using pandas and random libraries, it is a survival and death game of titanic according to probabilities.

  Language:Python2