Data is the oil for Uber. With data analysis tools and great insights, Uber improve its decisions, marketing strategy, promotional offers and predictive analytics. With more than 15 million rides per day across 600 cities in 65 countries, Uber is growing rapidly with Data Science starting from data visualization and gaining insights that help them to craft better decisions. Data Science tools play a key role in every operation of Uber.

The aim of this project is to build a classifier that can detect credit card fraudulent transactions using a variety of machine learning algorithms that will be able to discern fraudulent from non-fraudulent ones.

The aim of this interesting Data Science project is to build a recommendation system that recommends movies to the users. Let’s understand this with an example. Have you ever been on an online streaming platform like Netflix or Amazon Prime? If yes, then you must have noticed that after some time these platform starts recommending you different movies and TV shows according to your genre preference. This project in R programming is designed to help you understand the functioning of how a recommendation system works.

Customer segmentation is one of the most essential applications for all customer-facing industries (B2C companies). It uses the clustering algorithm of Machine Learning that allows companies to target the potential user base and also they can identify the best customers. It uses clustering techniques through which companies can identify the several segments of customers allowing them to target the potential user base for a specific campaign. Customer segmentation also uses K-means clustering algorithm which is essential for clustering unlabeled dataset.

Almost every data-driven organization is using the sentiment analysis model to determine the attitude of its customers toward the company products. In brief, it is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the consumer’s attitude towards a particular product or topic is positive, negative, or neutral. You will have to utilize the tiny text package to analyze the data and give scores to the corresponding words that are already present in the dataset.