Case Study, Soccer Data Analysis Data work in action Consider the various stages of data science systematically.Let's take a look at a real-world example of how data science works.Let's look at a European football dataset and analyze it. What will data science reveal about football, for example:- Generate statistics for a football dataset- Explain how to clean data and apply correlations to an existing dataset- Explain data visualization methods- Explain how to group similar groups and how creating these clusters helps the case study,as well as what conclusions were drawn based on this data analysis. Open datasets from the popular Kaggle website were used.This European database contains over 25,000 matches and over 10,000 players from the2008 to 2016 European professional football seasons. For football scenario, ideas associated with a better understanding of the players' strengths,improved performance, and player performance attributes.- I need to find the quickest way to improve my favorite player's performance.- How can we determine which characteristics have a greater impact on player performancethan others?The trainer can then put these ideas into practice. Create programs based on yourknowledge to increase the strength of the team. Key milestones to work The overall data science process consists of five key steps:- data collection,- data preparation,- data analysis,- presentation and reporting of findings,- and turning those ideas into data-driven actions.Any data science project will have similar steps. The specifics of each step may differ, butthe overall process remains consistent. As a first step in any data science activity, keep in mind that data can come from a variety ofsources. As new innovations emerge, the variety of data sources will only increase.Relative and NoSQL databases, text files in various data formats, and live online feeds frommachine sensors and online activity are all examples of broad categories.
In the football example, the dataset providers gathered data from various Internet sites andwent on a data collection and processing tour to prepare the data for analysis.Structured score data, lineup, team formation, and events, as well as betting odds data,player and team attributes, are all included in the data set. Dealing with data The plan was to import this dataset into Python. Python has well-defined data-receiving methods. Using a variety of sources.Among these sources are databases, data access APIs like the Twitter API, text files, andsensor data streams.Exploration of the data set is the next step in the data process. Python has libraries to help you explore your datasets during the data preparation stage. For example, you can generate a vital statistical summary of datasets such as mean andstandard deviation with just one line of code.As there are numerous issues in real-world datasets, data preparation also includes datacleaning. Cleaning can also be based on statistical analysis: removing emissions, missing values, andremoving unnecessary data from the data. Python provides data cleansing functions to aid incommon data cleansing tasks such as locating and removing null values. Data visualization is an effective way to capture your team's attention and get your messageacross in a short amount of time at every stage of the data processing.Python includes a number of open source data visualization libraries. This can greatlysimplify the task. The essence of data science is analysis. When the major preparatory steps have beencompleted, we will proceed to the algorithms. For dimensionality reduction, clustering, and regression, for example, there are numerousalgorithms and methods available: Python's Scikit-learn library includes a variety of machinelearning tools. The choice of function is based on the attributes that have the most influence on the problemat hand. To narrow down the number of features, some domain knowledge is required. In the case of football, if you are attempting to forecast player performance:- Blue characteristics for agility, reaction time, shot power, and running speed,- or green characteristics for hair or movie preferences?Similarly, if you divide the players into different groups.There are several advantages to narrowing the functions.- Models that are easier to understand- Models that learn much faster
- extension to newer scenarios Various functions have been organized. Python libraries like Scikit-learn restrictimplementation of machine learning algorithms at their most basic. The clustering algorithmK-Means from sklearn is used in the football example. Clustering refers to the grouping ofplayers. Into semantic sets that are similar, based on the attributes that have been selected. Importing the necessary K-means library into Python. The library is then used to parse ourdata. It is important to note that clustering is done in just one line here. Selecting the righttool for your analysis necessitates additional knowledge and understanding. Interpretation of results After we finished clustering, we divided the players into meaningful groups based on theattributes we chose.Let us now begin interpreting the results.- So, how do we interpret the findings?- Consider this: do they all have the same number of players?- How do these groups differ in terms of attributes?These clusters' construction can aid in the interpretation and presentation of these results.After doing all the data cleansing work, analysis and interpretation, it's time to present theconclusions. Most of the presentation or report explains how to interpret these results. Each group is distinct in that it differs from the other three in at least one way. Team coaches can use such findings to develop individual improvement strategies for eachgroup in order to act on them. There are numerous approaches and best practices for presenting or visualizing the results.We must decide on the type of chart, find a library or write your own, and include enoughdetails for the picture to be self-evident, such as label axes, legends, and readable font size. Results Considering a football data analysis example, we used a five-step data processing processto extract information from our original dataset.The procedure included the following steps:- obtaining results- data preparation,- analysis and presentation of results, and- use for data-driven actions.