- The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.
- Our aim here is to analyze the data set in detail and visualize it with a wide range of visualization tools.
- We will do:
- What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness?
- How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports?
- Did any country experience a significant increase or decrease in happiness?
- This dataset provides historical sales data for 45 stores located in different regions - each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.There are 3 csv files – Stores, Features and Sales.
- We are asked to:
- Predict the department-wide sales for each store for the following year
- Model the effects of markdowns on holiday weeks
- Provide recommended actions based on the insights drawn, with prioritization placed on largest business impac
- Data Exploration using House Sales data in Kings County.This data set contains house sale prices for King County, which includes Seattle, Washington.It includes homes sold between May 2014 and May 2015.
- Conduct an exploratory analysis,make assumptions about the missing data and impute values, make assumptions about the drivers of prices and see if your assumptions are valid or invalid.
Tools:scikit-learn, Pandas, Seaborn, Matplotlib, Pygame
➤ Dimonds
- Data Exploration using Dimonds dataset.This data set contains prices and other attributes for nearly 54,000 diamonds. Figures 1, 2, and 3 show the dimensions, color, and clarity used in the diamonds data set, respectively
- Conduct an exploratory analysis,make assumptions about the missing data and impute values, make assumptions about the drivers of prices and see if your assumptions are valid or invalid.
Tools:scikit-learn, Pandas, Seaborn, Matplotlib, Pygame