\n
import numpy as np \n import pandas as pd\n<\/code><\/pre>\n<\/div>\n\n- Python libraries are used to explore the dataset using graphs and plots:<\/li>\n<\/ul>\n
\n
import seaborn as sns\n import matplotlib.pyplot as plt\n<\/code><\/pre>\n<\/div>\n\n- Python libraries used to build the recommendation system model:<\/li>\n<\/ul>\n
\n
from sklearn.preprocessing import StandardScaler\n from sklearn.neighbors import NearestNeighbors\n from sklearn.decomposition import PCA\n<\/code><\/pre>\n<\/div>\n\n- Python library used to connect to a GridDB cluster:<\/li>\n<\/ul>\n
\n
import griddb_python as griddb\n<\/code><\/pre>\n<\/div>\nAfter successfully importing the required libraries, we begin with reading our English Premier League Players Dataset<\/strong>.<\/p>\nLoading the Dataset<\/strong><\/h2>\nGridDB plays a significant role in creating our recommendation system as it is the data storage mechanism we will use to store our dataset. To successfully store the data, we will first load the GridDB container. This can be done using the griddb_python<\/code> library we installed earlier. Next, we will use container.put<\/strong> to insert the data using a loop. Once done, we must load the data back into a data frame to continue creating our recommendation system.<\/p>\nThe code described in this section can be written as follows:<\/p>\n
\n
factory = griddb.StoreFactory.get_instance()\n\n# Provide the necessary arguments\ngridstore = factory.get_store(\n notification_member = '127.0.0.1:10001',\n cluster_name = 'myCluster',\n username = 'admin',\n password = 'admin'\n)\n\n# Define the container info\nconInfo = griddb.ContainerInfo(\n \"football_players\",\n [\n [\"name\", griddb.Type.STRING],\n [\"club\", griddb.Type.STRING],\n [\"age\", griddb.Type.DOUBLE],\n [\"position\", griddb.Type.STRING],\n [\"position_cat\", griddb.Type.DOUBLE],\n [\"market_value\", griddb.Type.DOUBLE],\n [\"page_views\", griddb.Type.DOUBLE],\n [\"fpl_value\", griddb.Type.DOUBLE],\n [\"fpl_sel\", griddb.Type.STRING],\n [\"fpl_points\", griddb.Type.DOUBLE],\n [\"region\", griddb.Type.DOUBLE],\n [\"nationality\", griddb.Type.STRING],\n [\"new_foreign\", griddb.Type.DOUBLE],\n [\"age_cat\", griddb.Type.DOUBLE],\n [\"club_id\", griddb.Type.DOUBLE],\n [\"big_club\", griddb.Type.DOUBLE],\n [\"new_signing\", griddb.Type.DOUBLE]\n ],\n griddb.ContainerType.COLLECTION, True\n)\n\n# Drop container if it exists\ngridstore.drop_container(conInfo.name)\n\n# Create a container\ncontainer = gridstore.put_container(conInfo)\n\n# Load the data\n\n# Put rows\nfor i in range(len(data)):\n row = data.iloc[i].tolist()\n try:\n container.put(row)\n except Exception as e:\n print(f\"Error on row {i}: {row}\")\n print(e)\n\ncont = gridstore.get_container(\"football_players\")\n\nif cont is None:\n print(\"Does not exist\")\n\nprint(\"connection successful\")\n\n# Define the exact columns you need\ncolumns = [\"*\"]\n\nselect_statement = \"SELECT \" + \", \".join(columns) + \" FROM football_players\"\n\n# Execute the query\nquery = container.query(select_statement)\nrs = query.fetch(False)\n\ndata = rs.fetch_rows()\n\nprint(data.head())<\/code><\/pre>\n<\/div>\nExploratory Data Analysis<\/strong><\/h2>\nBefore we build our recommendation system, we must begin with an exploratory data analysis that will allow us to find any inconsistencies in our data and overall visualization of the dataset.
\nFirst, we begin by checking for any null values in our attributes. This is achieved with the following lines of code:<\/p>\n
\n
data.isnull().sum()<\/code><\/pre>\n<\/div>\nThis cell outputs the following results, indicating that we have one<\/strong> missing value for the region<\/strong> attribute:<\/p>\n\n
name 0\nclub 0\nage 0\nposition 0\nposition_cat 0\nmarket_value 0\npage_views 0\nfpl_value 0\nfpl_sel 0\nfpl_points 0\nregion 1\nnationality 0\nnew_foreign 0\nage_cat 0\nclub_id 0\nbig_club 0\nnew_signing 0\ndtype: int64<\/code><\/pre>\n<\/div>\nTo clean up our missing value, we can use the built-in method dropna()<\/code>. This method returns the newly cleaned dataset to replace the old unclean one.<\/p>\nThis is achieved with the following code:<\/p>\n
\n
data = data.dropna()<\/code><\/pre>\n<\/div>\nNow that we have replaced all missing values, we can move to visualize our data. The first step to graph our data is to compute the correlation matrix representing the different attributes that can be used to predict others using the corr()<\/code> method. This is very useful as it shows what data points can be used to recommend a player and how every player is measured in terms of the attributes presented in our dataset.<\/p>\nThis is achieved with the following code:<\/p>\n
\n
sample = data.select_dtypes(include='number')\ncorr = sample.corr()\nmask = np.zeros_like(corr, dtype = np.bool_)\nmask[np.triu_indices_from(mask)] = True\nplt.figure(figsize=(10,10))\nsns.heatmap(corr, mask=mask)<\/code><\/pre>\n<\/div>\nThe graph we came up with in this section is a heatmap that takes the X<\/strong> and Y<\/strong> axes’ attributes and measures correlation in a predetermined scale. The following is our heatmap:<\/p>\n![\"image\"](\"https:\/\/github.com\/SimoCs\/Soccer-Players-Recommendation-System\/assets\/32298957\/c863ddd6-7b06-47cd-b238-edd48aa10dcb\")
<\/p>\n
Recommendation System<\/strong><\/h2>\nIn this section, we design and prepare our recommendation system model. For this purpose, we begin by converting our data points to numerical values using a standard scaler. Next, we run the Nearest Neighbors model that will be used as our recommendation system algorithm. The model will take as input our numerical data and will be used to find comparable players that are close in characteristics to our input.<\/p>\n
This is achieved with the following code:<\/p>\n
\n
scaled = StandardScaler()\nX = scaled.fit_transform(sample)\nrecommendations = NearestNeighbors(n_neighbors = 5, algorithm='kd_tree')\nrecommendations.fit(X)\nplayer_index = recommendations.kneighbors(X)[1]<\/code><\/pre>\n<\/div>\nWe have prepared our model and must create a method to find the player’s index in our dataset based on a given name. This is very useful as it will allow us to use the results of the recommendation system to extract the recommended player attributes.<\/p>\n
This is achieved with the following code:<\/p>\n
\n
def find_index(x):\n return data[data['name']==x].index.tolist()[0]<\/code><\/pre>\n<\/div>\nThe last step is to create a method using our model to extract the player’s information. In other words, we must extract key values describing the recommended players for every player. Our example mainly focuses on the player’s name, market value, age, and current club.<\/p>\n
This is achieved with the following code:<\/p>\n
\n
def recommendation_system(player):\n print(\"Here are four players who are similar to {}: \".format(player))\n index = find_index(player)\n \n for i in player_index[index][1:]:\n print(\"Player Name: {}nPlayer Market Value: \u20ac{}nPlayer Age: {}nPlayer Current Club: {}n\".format(\n data.iloc[i]['name'],\n data.iloc[i]['market_value'], \n data.iloc[i]['age'], \n data.iloc[i]['club']))<\/code><\/pre>\n<\/div>\nModel Evaluation<\/h2>\n
At this moment, we are ready to evaluate our model. In this example, we will run our recommendation system that inputs a player name and recommends four<\/strong> key players comparable to the player inputter.<\/p>\nThis code can be achieved as follow:<\/p>\n
\n
recommendation_system('Petr Cech')<\/code><\/pre>\n<\/div>\nThe results of our mode are as follows:<\/p>\n
\n
Here are four players who are similar to Petr Cech: \nPlayer Name: Willy Caballero\nPlayer Market Value: \u20ac1.5\nPlayer Age: 35\nPlayer Current Club: Chelsea\n\nPlayer Name: Nacho Monreal\nPlayer Market Value: \u20ac13.0\nPlayer Age: 31\nPlayer Current Club: Arsenal\n\nPlayer Name: Laurent Koscielny\nPlayer Market Value: \u20ac22.0\nPlayer Age: 31\nPlayer Current Club: Arsenal\n\nPlayer Name: Artur Boruc\nPlayer Market Value: \u20ac1.0\nPlayer Age: 37\nPlayer Current Club: Bournemouth<\/code><\/pre>\n<\/div>\nAs we can see, the recommendation system was able to predict with high accurse five players that are very close to our input players. This proves that our model is ready for deployment and can be used to predict future talent based on a current player.<\/p>\n
Conclusion<\/strong><\/h2>\nUsing a recommendation system to create a list of prospects in the business of professional atheism is a competitive advantage any team manager should consider. The article covered the step-by-step process of creating a recommendation model using English Premier League Players. GridDB was extensively used in the article as a database storage for our dataset. This database stores trained data, and it is used to call our recommendation system to recommend players based on their peers.<\/p>\n","protected":false},"excerpt":{"rendered":"
The soccer business is a multi-billion dollar industry combining high-performing athletes, passionate fans, and big sponsorship deals. Team owners and managers around the globe are always looking for an edge to find the best talent that can add to the winning soccer to their team. Using machine learning to help team managers find a balanced, […]<\/p>\n","protected":false},"author":41,"featured_media":29735,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46772","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\n
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