Edureka's Data Science Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles.
Project#1: Movies Collection
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. You will explore the following:
- 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 the 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, decide on the price of new apartments.
Project #3: Diabetes Prediction
Description: The Use-Case focuses on making predictions based on the patient’s characteristic data set, the dataset contains attributes such as "glucose level", "blood pressure", "age" etc. At last, the goal is to make a high accuracy machine learning model to 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 historical transaction data, 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. You have to do the following:
- Perform Sentiment analysis on the tweets obtained and visualize the conclusions.
- Compare two football clubs, based on 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.You have to make informed decisions on staffing, hospitality and pricing for tickets.