amaha2428 / communicate-findings-on-fifa-19

This project was given as part of Udacity's Data Analyst Nanodegree program

FIFA 19 Data Exploration

Dataset:

The FIFA 19 data set consist of information about players with features such as Age, Nationality, Club, Body Type, value and many other important feature about a player. The dataset can be found here: https://gitlab.com/Medonati/udacity-data-analyst-nanodegree/-/blob/master/Project%205%3A%20Communicate%20Data%20Findings/data.xlsx

Summary of Findings:

From the quick descriptive statistics gotten, The minimum age of a player is 16yrs and 75% of players are below 29 yrs of age. The maximum weight of a player is 243lbs while 50% of players have stamina rating of 66. I later went on to carry out univariant exploration where i looked at variables such as Weight, Age, Nationality etc. We were able to see that majority of players are from England and the age distribution accross the dataset was rightly skewed and also the weight distribution was normal. I went on to look for more insight on correlation with different features such as weight and finishing, ball control and finishing, dribbling and finishing and interestingly, there was a positive correlation between dribbling and finishing. Also, i went on to find more insight on players Body Type, value and Preferred foot and noticed that letf footed stocky players were valued more.

Key Insights:

I decided to look into players age, body type, nationality, wage and value for the presentation. i looked at the distribution of players age and also Nationality, players wage and preferred foot of players using barplots. Clustered barplots was used to look into a players value with respect to his preferred foot and despite most of the players being right footed, left footed players are valued more.

This project was given as part of Udacity's Data Analyst Nanodegree program