Towards the end of the Course, you will be working on a live project. We will emphasize the concepts learned in the various Modules through different case studies. The various case studies are listed below:
Project#1: Movie Dataset
Industry: Entertainment Industry
Description: The goal of this Use-Case is to explore the movie dataset, given the parameters like: "duration", "movie title", "gross collection", "budget", "title year", etc.
• Know top ten movies with the highest profits.
• Know top rated movies in the list and average IMDB score.
• Plot a graphical representation to show number of movies released each year.
• Group the movies into clusters based on the Facebook likes.
• Group the directors based on movie collection and budget.
Project #2: Real Estate price prediction
Industry: Business Intelligence and Analytics
Description: The goal of this Use-case is to make predictions using Real Estate market data. The dataset contains the of the price of apartments in Boston. This data contains values such as crime rate, age, accessibility, population etc.
• Based on this data, the company wants to decide on the price of new apartments.
Project #3: Diabetes Prediction
Description: The Use-Case focuses on making predictions based upon the patient’s characteristic data set, the data set contains attributes such as glucose level, blood pressure, age, etc. At last the goal is to make a high accuracy machine learning model which can predict, whether a patient is Diabetic or not.
Project #4: Recommendation System for Grocery store
Industry: Food Retail industry
Description: The Use-Case scenario is to create recommendations for customers of a grocery store based upon historic transactional data, the goal is to create a recommendation engine which could recommend preferable articles.
Project #5: Twitter Analytics
Industry: Social Media Analytics
Description: This Use-Case focuses on social media analytics. The problem can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. The task is to perform Sentiment analysis on the tweets obtained and visualize the conclusions. In this Use-Case we will compare two football clubs, based upon the tweets they are receiving from their fans.
Project #6: Air Passengers forecasting
Industry: Commercial Aviation
Description: This Use-Case is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month the dataset we will analyze contains monthly totals of international airline passengers between 1949 to 1960.
This information can help management to make informed decisions on staffing, hospitality and pricing for tickets.