import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import eli5 from eli5.sklearn import PermutationImportance from collections import Counter import missingno as msno import warnings warnings.filterwarnings('ignore') import plotly sns.set_style('darkgrid')

df=pd.read_csv('../input/data.csv')

df.head().T

df.columns

df.info()

df.describe().T

df.drop(['Unnamed: 0','Photo','Flag','Club Logo'],axis=1,inplace=True)

msno.bar(df.sample( 18207 ),(28,10),color='red')

df.isnull().sum()

missing_height = df[df['Height'].isnull()].index.tolist() missing_weight = df[df['Weight'].isnull()].index.tolist() if missing_height == missing_weight: print('They are same') else: print('They are different')

df.drop(df.index[missing_height],inplace =True)

df.isnull().sum()

df.drop(['Loaned From','Release Clause','Joined'],axis=1,inplace=True)

#Number of countries available and top 5 countries with highest number of players print('Total number of countries : {0}'.format(df['Nationality'].nunique())) print(df['Nationality'].value_counts().head(5)) print('--'*40) print("\nEuropean Countries have most players")

#Total number of clubs present and top 5 clubs with highest number of players print('Total number of clubs : {0}'.format(df['Club'].nunique())) print(df['Club'].value_counts().head(5))

#Player with maximum Potential and Overall Performance print('Maximum Potential : '+str(df.loc[df['Potential'].idxmax()][1])) print('Maximum Overall Perforamnce : '+str(df.loc[df['Overall'].idxmax()][1]))

pr_cols=['Crossing', 'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration', 'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower', 'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure', 'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes'] print('BEST IN DIFFERENT ASPECTS :') print('_________________________\n\n') i=0 while i < len(pr_cols): print('Best {0} : {1}'.format(pr_cols[i],df.loc[df[pr_cols[i]].idxmax()][1])) i += 1

#Cleaning some of values so that we can interpret them def value_to_int(df_value): try: value = float(df_value[1:-1]) suffix = df_value[-1:] if suffix == 'M': value = value * 1000000 elif suffix == 'K': value = value * 1000 except ValueError: value = 0 return value df['Value'] = df['Value'].apply(value_to_int) df['Wage'] = df['Wage'].apply(value_to_int)

df.head().T

#Top earners print('Most valued player : '+str(df.loc[df['Value'].idxmax()][1])) print('Highest earner : '+str(df.loc[df['Wage'].idxmax()][1])) print("--"*40) print("\nTop Earners")

sns.jointplot(x=df['Age'],y=df['Potential'], joint_kws={'alpha':0.1,'s':5,'color':'red'}, marginal_kws={'color':'red'})

sns.lmplot(data = df, x = 'Age', y = 'SprintSpeed',lowess=True,scatter_kws={'alpha':0.01, 's':5,'color':'green'}, line_kws={'color':'red'})

sns.lmplot(x = 'BallControl', y = 'Dribbling', data = df,col = 'Preferred Foot',scatter_kws = {'alpha':0.1,'color':'orange'}, line_kws={'color':'red'})

sns.jointplot(x=df['Dribbling'], y=df['Crossing'], kind="hex", color="#4CB391");

value = df.Value cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True) sns.relplot(x="Age", y="Potential", hue=value/100000, sizes=(40, 400), alpha=.5, height=6, data=df);

corr = df.corr() mask = np.zeros_like(corr) mask[np.triu_indices_from(mask)] = True with sns.axes_style("white"): f, ax = plt.subplots(figsize=(15, 15)) ax = sns.heatmap(corr,mask=mask,square=True,linewidths=.8,cmap="YlGnBu")

plt.rcParams['figure.figsize'] = (20, 7) plt.style.use('seaborn-dark-palette') sns.boxenplot(df['Overall'], df['Age'], hue = df['Preferred Foot'], palette = 'rocket') plt.title('Comparison of Overall Scores and age wrt Preferred foot', fontsize = 20) plt.show()

cols = ['Age','Overall','Potential','Acceleration','SprintSpeed',"Agility","Stamina",'Strength','Preferred Foot'] df_small = df[cols]

df_small.head()

sns.pairplot(df_small, hue ='Preferred Foot',palette=["black", "red"],plot_kws=dict(s=50, alpha =0.8),markers=['^','v'])

df=pd.read_csv('../input/data.csv')

#DROP UNNECESSARY VALUES drop_cols = df.columns[28:54] df = df.drop(drop_cols, axis = 1) df = df.drop(['Unnamed: 0','ID','Photo','Flag','Club Logo','Jersey Number','Joined','Special','Loaned From','Body Type', 'Release Clause', 'Weight','Height','Contract Valid Until','Wage','Value','Name','Club'], axis = 1) df = df.dropna() df.head()

#Turn Real Face into a binary indicator variable def face_to_num(df): if (df['Real Face'] == 'Yes'): return 1 else: return 0 #Turn Preferred Foot into a binary indicator variable def right_footed(df): if (df['Preferred Foot'] == 'Right'): return 1 else: return 0 #Create a simplified position varaible to account for all player positions def simple_position(df): if (df['Position'] == 'GK'): return 'GK' elif ((df['Position'] == 'RB') | (df['Position'] == 'LB') | (df['Position'] == 'CB') | (df['Position'] == 'LCB') | (df['Position'] == 'RCB') | (df['Position'] == 'RWB') | (df['Position'] == 'LWB') ): return 'DF' elif ((df['Position'] == 'LDM') | (df['Position'] == 'CDM') | (df['Position'] == 'RDM')): return 'DM' elif ((df['Position'] == 'LM') | (df['Position'] == 'LCM') | (df['Position'] == 'CM') | (df['Position'] == 'RCM') | (df['Position'] == 'RM')): return 'MF' elif ((df['Position'] == 'LAM') | (df['Position'] == 'CAM') | (df['Position'] == 'RAM') | (df['Position'] == 'LW') | (df['Position'] == 'RW')): return 'AM' elif ((df['Position'] == 'RS') | (df['Position'] == 'ST') | (df['Position'] == 'LS') | (df['Position'] == 'CF') | (df['Position'] == 'LF') | (df['Position'] == 'RF')): return 'ST' else: return df.Position #Get a count of Nationalities in the Dataset, make of list of those with over 250 Players (our Major Nations) nat_counts = df.Nationality.value_counts() nat_list = nat_counts[nat_counts > 250].index.tolist() #Replace Nationality with a binary indicator variable for 'Major Nation' def major_nation(df): if (df.Nationality in nat_list): return 1 else: return 0 #Create a copy of the original dataframe to avoid indexing errors df1 = df.copy() #Apply changes to dataset to create new column df1['Real_Face'] = df1.apply(face_to_num, axis=1) df1['Right_Foot'] = df1.apply(right_footed, axis=1) df1['Simple_Position'] = df1.apply(simple_position,axis = 1) df1['Major_Nation'] = df1.apply(major_nation,axis = 1) #Split the Work Rate Column in two tempwork = df1["Work Rate"].str.split("/ ", n = 1, expand = True) #Create new column for first work rate df1["WorkRate1"]= tempwork[0] #Create new column for second work rate df1["WorkRate2"]= tempwork[1] #Drop original columns used df1 = df1.drop(['Work Rate','Preferred Foot','Real Face', 'Position','Nationality'], axis = 1) df1.head()

#Split ID as a Target value target = df1.Overall df2 = df1.drop(['Overall'], axis = 1) #Splitting into test and train from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(df2, target, test_size=0.2) #One Hot Encoding X_train = pd.get_dummies(X_train) X_test = pd.get_dummies(X_test) print(X_test.shape,X_train.shape) print(y_test.shape,y_train.shape)

#Applying Linear Regression from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) #Finding the r2 score and root mean squared error from sklearn.metrics import r2_score, mean_squared_error print('r2 score: '+str(r2_score(y_test, predictions))) print('RMSE : '+str(np.sqrt(mean_squared_error(y_test, predictions))))

perm = PermutationImportance(model, random_state=1).fit(X_test, y_test) eli5.show_weights(perm, feature_names = X_test.columns.tolist()) #Top 3 important features are Potential, Age & Reactions

#Visualising the results plt.figure(figsize=(18,10)) sns.regplot(predictions,y_test,scatter_kws={'color':'red','edgecolor':'blue','linewidth':'0.7'},line_kws={'color':'black','alpha':0.5}) plt.xlabel('Predictions') plt.ylabel('Overall') plt.title("Linear Prediction of Player Rating") plt.show()