ML_Projects_

This is a compilation of ML projects, most kaggle competitions

Folder structure

• Classification: is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output Y = f(X)

  • Breast cancer: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data#data.csv
    • 31 Features
    • diagnosis - The diagnosis of breast tissues (M = malignant, B = benign)
  • Credit Card: https://www.kaggle.com/mlg-ulb/creditcardfraud
    • 30 Features
    • Class 1 for fraudulent transactions, 0 otherwise
  • Diabetes: https://www.kaggle.com/uciml/pima-indians-diabetes-database
    • 8 Features
    • Outcome - Class variable (0 or 1) 268 of 768 are 1, the others are 0
  • Heart: https://www.kaggle.com/ronitf/heart-disease-uci
    • 13 Features
    • target: 1 or 0
  • Kidney Disease: https://www.kaggle.com/mansoordaku/ckdisease
    • 25 Features
    • Target - 'ckd' or 'notckd' - ckd=chronic kidney disease.

• Regression: A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”

  • AirBnB NYC: https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data
    • 16 Features
    • Target - price in dollars
  • Avocado prices: https://www.kaggle.com/neuromusic/avocado-prices
    • 13 Features
    • Target - Total number of avocados sold
  • SF Salaries: https://www.kaggle.com/kaggle/sf-salaries
    • 12 Features
    • Target - Total Pay salary
  • House price: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
    • 80 Features
    • Target - SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.
  • Women Shoes Prices: https://www.kaggle.com/datafiniti/womens-shoes-prices
    • 50 Features
    • Target - Prices amount min and Prices amount max

• Recommendation: is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications.

  • Retailrocket : https://www.kaggle.com/retailrocket/ecommerce-dataset
    • 10 Features (4 files)
    • Target - value: property value of the item
  • Netflix movies: https://www.kaggle.com/shivamb/netflix-shows
    • 12 Features
    • Target - rating. TV Rating of the movie / show
  • Movielens: https://www.kaggle.com/grouplens/movielens-20m-dataset#rating.csv
    • 6 Features
    • Target - movie ratings
  • Amazon Product Reviews: https://www.kaggle.com/saurav9786/amazon-product-reviews#ratings_Electronics%20(1).csv
    • 3 Features
    • Target - Rating :Rating of the corresponding product by the corresponding user
  • TED Talks: https://www.kaggle.com/rounakbanik/ted-talks
    • 16 Features
    • Target - Rating : A stringified dictionary of the various ratings given to the talk (inspiring, fascinating, jaw dropping, etc.)

• Unsupervised: is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.

  • College: https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data#College.csv
    • 19 Features
    • No Target but it needs: Cluster private information
  • Department of justice (json): https://www.kaggle.com/jbencina/department-of-justice-20092018-press-releases/
    • 6 Features
    • No Target but it needs: What words tend occur frequently together?
  • Credit card fraud detection: https://www.kaggle.com/arjunbhasin2013/ccdata#CC%20GENERAL.csv
    • 18 Features
    • No Target but it needs: Fraud or not
  • Wines: https://www.kaggle.com/akram24/wine-pca
    • 14 Features
    • No Target but it needs: Cluster types of wines
  • World happiness: https://www.kaggle.com/unsdsn/world-happiness
    • 12 Features
    • No Target but it needs: happiness or not

• Others with text (NLP): NLP is like learning the language of your own mind!

  • Tweets: https://www.kaggle.com/c/nlp-getting-started/data
    • 4 Features
    • target - in train.csv only, this denotes whether a tweet is about a real disaster (1) or not (0)
  • Youtube: https://www.kaggle.com/datasnaek/youtube-new
    • 16 Features
    • 21 files for analysing sentiments
  • Seattle hotel: https://github.com/0x6f736f646f/Seattle-Hotel-recommendation/blob/master/Data/Seattle_Hotels.csv
    • 3 Features
    • Target - desc

Authors

• Mariana Alanis - Initial work -