GitHub - malakalimah/soccer-data-base: In this project I analyzed data of soccer data base from (https://www.google.com/url?q=https://www.kaggle.com/hugomathien/soccer&sa=D&ust=1532469042123000 ) This data contains 8 tables : table shape dtypes Player_attributes (183978, 42) float64(35), int64(3), object(4) player 11060 rows × 7 columns float64(1), int64(4), object(2) Match 25979 rows × 115 columns float64(96), int64(9), object(10) league 11 rows x 3columns int64(2), object(1) country (11, 2) int64(1), object(1) team 299 rows × 5 columns float64(1), int64(2), object(2) Team_Attributes 1458 rows × 25 columns float64(1), int64(11), object(13) After wrangling those tables we will as k the following questions : Questions What teams improved the most over the time period? Which players had the most penalties? What team attributes lead to the most victories? what is the impact of playing in home land or remotely in different seasons ? after asking these questions we get out with their answers which can be simply represented in visualizations

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
Soccer data base analysis .pdf		Soccer data base analysis .pdf
database.rar		database.rar
soccer project.ipynb		soccer project.ipynb
world_coordinates.csv		world_coordinates.csv

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In this project I analyzed data of soccer data base from (https://www.google.com/url?q=https://www.kaggle.com/hugomathien/soccer&sa=D&ust=1532469042123000 ) This data contains 8 tables : table shape dtypes Player_attributes (183978, 42) float64(35), int64(3), object(4) player 11060 rows × 7 columns float64(1), int64(4), object(2) Match 25979 row…