Thursday, June 14, 2018

Free 50 Datasets to learn Big Data and Machine Learning

Big Data + Machine Learning
Ticket History
Usage Patte...
Dataset Finders
Kaggle: A data science site that contains a variety of externally-contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses.
UCI Machine Learning Repository: One of the oldest sources of datasets on the web, and a great first stop when looking for interesting datasets. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. You can download data directly from the UCI Machine Learning repository, without registration.

General Datasets
Public Government datasets This site makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Be warned though: much of the data requires additional research.
Food Environment Atlas: Contains data on how local food choices affect diet in the US.
School system finances: A survey of the finances of school systems in the US.
Chronic disease data: Data on chronic disease indicators in areas across the US.
The US National Center for Education Statistics: Data on educational institutions and education demographics from the US and around the world.
The UK Data Centre: The UK’s largest collection of social, economic and population data.
Data USA: A comprehensive visualization of US public data.
Finance & Economics
Quandl: A good source for economic and financial data – useful for building models to predict economic indicators or stock prices.
World Bank Open Data: Datasets covering population demographics and a huge number of economic and development indicators from across the world.
IMF Data: The International Monetary Fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices and investments.
Financial Times Market Data: Up to date information on financial markets from around the world, including stock price indexes, commodities and foreign exchange.
Google Trends: Examine and analyze data on internet search activity and trending news stories around the world.
American Economic Association (AEA): A good source to find US macroeconomic data.

Machine Learning Datasets:
Labelme: A large dataset of annotated images.
ImageNet: The de-facto image dataset for new algorithms. Is organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images.
LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.)
MS COCO: Generic image understanding and captioning.
COIL100 : 100 different objects imaged at every angle in a 360 rotation.
Visual Genome: Very detailed visual knowledge base with captioning of ~100K images.
Google’s Open Images: A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.
Labelled Faces in the Wild: 13,000 labeled images of human faces, for use in developing applications that involve facial recognition.
Stanford Dogs Dataset: Contains 20,580 images and 120 different dog breed categories.
Indoor Scene Recognition: A very specific dataset, useful as most scene recognition models are better ‘outside’. Contains 67 Indoor categories, and a total of 15620 images.
Sentiment Analysis
Multidomain sentiment analysis dataset: A slightly older dataset that features product reviews from Amazon.
IMDB reviews: An older, relatively small dataset for binary sentiment classification, features 25,000 movie reviews.
Stanford Sentiment Treebank: Standard sentiment dataset with sentiment annotations.
Sentiment140: A popular dataset, which uses 160,000 tweets with emoticons pre-removed.
Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets
Natural Language Processing
Enron Dataset: Email data from the senior management of Enron, organized into folders.
Amazon Reviews: Contains around 35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.
Google Books Ngrams: A collection of words from Google books.
Blogger Corpus: A collection 681,288 blog posts gathered from Each blog contains a minimum of 200 occurrences of commonly used English words.
Wikipedia Links data: The full text of Wikipedia. The dataset contains almost 1.9 billion words from more than 4 million articles. You can search by word, phrase or part of a paragraph itself.
Gutenberg eBooks List: Annotated list of ebooks from Project Gutenberg.
Hansards text chunks of Canadian Parliament: 1.3 million pairs of texts from the records of the 36th Canadian Parliament.
Jeopardy: Archive of more than 200,000 questions from the quiz show Jeopardy.
SMS Spam Collection in English: A dataset that consists of 5,574 English SMS spam messages
Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.
UCI’s Spambase: A large spam email dataset, useful for spam filtering.
Berkeley DeepDrive BDD100k: Currently the largest dataset for self-driving AI. Contains over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. The annotated images come from New York and San Francisco areas.
Baidu Apolloscapes: Large dataset that defines 26 different semantic items  such as cars, bicycles, pedestrians, buildings, street lights, etc. More than 7 hours of highway driving. Details include car’s speed, acceleration, steering angle, and GPS coordinates.
Oxford’s Robotic Car: Over 100 repetitions of the same route through Oxford, UK, captured over a period of a year. The dataset captures different combinations of weather, traffic and pedestrians, along with long-term changes such as construction and roadworks.
Cityscape Dataset: A large dataset that records urban street scenes in 50 different cities.
CSSAD Dataset: This dataset is useful for perception and navigation of autonomous vehicles. The dataset skews heavily on roads found in the developed world.
KUL Belgium Traffic Sign Dataset: More than 10000+ traffic sign annotations from  thousands of physically distinct traffic signs in the Flanders region in Belgium.
MIT AGE Lab: A sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.
LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets: This dataset includes traffic signs, vehicles detection, traffic lights, and trajectory patterns.

Friday, June 1, 2018

The Impact of Big Data Analytics in the Retail Industry

Retail is one of the fastest-moving non-contractual business sectors. It is highly competitive and is an industry where innovation is constantly transforming the landscape. If you are part of the retail industry, you may have experienced constant changes and business models once celebrated becoming obsolete overnight.Image result for The Impact of Big Data Analytics in the Retail Industry
You may, however, be delighted to know that proactive investment in technologies such as machine learning, big data, and artificial intelligence can give your business a competitive edge to stay ahead of the competition.
Artificial intelligence has the potential to facilitate better customer experiences through machine learning, augmented reality, virtual reality and voice processing. According to Gartner, by 2020, AI will manage 85% of the customer interactions in retail.
The application of machine learning and AI systems in retail was recently demonstrated by companies like Amazon using the "Amazon Go" grocery store and by Walmart through "Shelf Scanning Robots."
If these are just brushed aside as proofs of concept today, we don’t know yet how far these technologies will take us. Companies like Amazon and Walmart are large but are not complacent, and they constantly invest in technology, thereby creating huge competition for the relatively smaller online stores and brick-and-mortar stores.

Challenges Faced by Small- and Medium-Sized Retailers

  • Small- and medium-sized retailers are struggling to offer a better shopping experience and provide customer satisfaction with limited budgets.
  • Small- and medium-sized retailers are unable to invest in understanding customer perceptions, leveraging strengths, and addressing weaknesses so much so that some do not even identify their customer using a lightweight loyalty program.
  • Small- and medium-sized retailers do not assign enough resources to identify profitable customers and tailor marketing and service efforts nor do they identify customers or prospects with future high potential.
  • Many of them have not had the time to try to increase marketing ROI for every dollar spent.
  • Personalization and product recommendations are offered by large companies at an individual customer level to enhance conversions, and minimizing cart abandonment is difficult for small- and medium-sized retailers.
  • Small- and medium-sized retailers also do not have resources to build solutions that optimize inventory planning for perishable/semi-perishable goods nor to ensure the availability of the right products for end customers.

How Small- and Medium-Sized Retailers Can Compete

By empowering individuals across the organization to make decisions accurately and confidently by harnessing big data, these retailers can perceive customers more deeply and uncover hidden trends that reveal new opportunities. Big data analytics has applications at every stage and can help with predicting trends (seasonal and otherwise) and demand, thus isolating customer interest and understanding and predicting customer behavior.
Let's take a look at some common techniques that are useful for the retail industry.

Customer Behavior and Predictive Analytics

You can use data analytics to find your potential customers, the key drivers that motivate them to buy more, and the best way to reach them. There is an opportunity to interact with customers through multiple channels like social media, e-commerce, or in-person at the store. In addition, location analytics can be used in-store to help better understand people’s purchasing behavior and to monitor consumer traffic. A customer’s purchase and browsing history (both in-store and online) can be used to predict the needs and interests and personalize promotions for them.

Operational Analytics and Supply Chain Analysis

Retailers can use analytics to optimize supply chains and product distribution to scale back prices. You can potentially combine structured data with unstructured data and then use this data to discover outliers, runtime series, and root cause analyses, and parse, remodel and visualize data.
A few other data-driven approaches include:
  • Text mining algorithms to arrive items and order quantity automatically.
  • Deep learning techniques such as convoluted nets for recognition and analysis of images obtained from refrigerator cameras and automation of order placement.
  • Use of text mining to conduct customer sentiment analysis.
  • Customer lifetime value (CLTV) scores to identify specific customers who need to be targeted or reactivated.
  • Use of look-alike modeling on third-party databases to identify profiles similar to high-value customers (obtained through CLTV analysis or based on the share of wallet maximization).
  • Creation of unique customer personas.
  • Past purchase behavior and timing analysis to identify potential products that customers are most likely to purchase.
  • Suggest more relevant product recommendations based on customer personas and purchase behavior.
  • Enhanced end-user experience by suggesting the right products at the right time of the day.