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import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn import preprocessing from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report,accuracy_score from sklearn.model_selection import TimeSeriesSplit,train_test_split from sklearn.cluster import KMeans import matplotlib from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report,accuracy_score from sklearn.naive_bayes import GaussianNB from sklearn import metrics from sklearn.svm import LinearSVC import pylab as pl from sklearn.ensemble import RandomForestClassifier import warnings warnings.filterwarnings('ignore')
df=pd.read_csv("athlete_events.csv")
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df
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df.head()
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df.info()
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df.describe()
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df.dtypes
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df.ndim
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df.shape
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df.isna().sum()
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#DNW:Did Not win , missing values of medal are filled with DNW df['Medal'].fillna("DNW",inplace=True)
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df_noc=pd.read_csv("noc_regions.csv")
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df_noc
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df_noc=df_noc.drop("notes",axis=1)
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df_noc
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df_noc.rename(columns={"region":"country"},inplace=True)
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df_noc
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df.sample(4)
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#joining both dataset olympics_merge=df.merge(df_noc,left_on='NOC',right_on='NOC',how='left')
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olympics_merge.sample()
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print(olympics_merge.loc[olympics_merge['country'].isnull(),['NOC', 'Team']].drop_duplicates())
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Replace missing Teams by the values 1. SGP - Singapore
# 2. ROT - Refugee Olympic Athletes
# 3. UNK - Unknown
# 4. TUV - Tuvalu
#olympics_merge.loc[olympics_merge['Country'].isnull(), ['Country']] = olympics_merge['Team']
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olympics_merge.loc[olympics_merge['country'].isnull(), ['country']] = olympics_merge['Team']
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olympics_merge
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print(olympics_merge.loc[olympics_merge['country'].isnull(),['NOC', 'Team']].drop_duplicates())
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olympics_merge['country'] = np.where(olympics_merge['NOC']=='SGP', 'Singapore', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='ROT', 'Refugee Olympic Athletes', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='UNK', 'Unknown', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='TUV', 'Tuvalu', olympics_merge['country'])
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olympics_merge
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olympics_merge.drop("Team",axis=1,inplace=True)
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olympics_merge.sample()
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olympics_merge.rename(columns={'country':'Team'},inplace=True)
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olympics_merge.head(2)
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print(olympics_merge.loc[olympics_merge['Team'].isnull(),['NOC', 'Team']].drop_duplicates())
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olympics_merge.isnull().sum()
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for i in ["Age","Height","Weight"]: sns.histplot(olympics_merge[i],kde=True) plt.show()
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for i in ["Age","Weight",]: olympics_merge[i]=olympics_merge[i].fillna(olympics_merge[i].mean())
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olympics_merge["Height"]=olympics_merge["Height"].fillna(olympics_merge["Height"].mean())
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olympics_merge.isnull().sum()
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olympics_merge.info()
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olympics_merge['Sex']=np.where(olympics_merge['Sex']=='M',1,0)
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olympics_merge.sample(2)
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olympics_merge["Medal"].unique()
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olympics_merge['Event'].unique()
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olympics_merge['Sport'].unique()
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olympics_merge1=olympics_merge
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olympics_merge1
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from sklearn.preprocessing import LabelEncoder le=LabelEncoder()
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olympics_merge1['Medal']=le.fit_transform(olympics_merge1['Medal'])
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olympics_merge1
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olympics_merge1['Medal'].unique()
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summer=olympics_merge1.loc[(olympics_merge1['Year']>1960)&(olympics_merge1['Season']=="Summer"), :] summer.head(5)
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summer=summer.reset_index() summer.head(10)
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summer.sample()
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#extracting unique events in a new list
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summerlistunique=summer.Event.unique() len(summerlistunique)
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summerlistunique
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summer.drop(['Season'],axis=1,inplace=True) summer.drop(['NOC'],axis=1,inplace=True) summer.drop(['Games'],axis=1,inplace=True) summer.drop(['City'],axis=1,inplace=True) summer.drop(['Year'],axis=1,inplace=True) summer.drop(['Sport'],axis=1,inplace=True) summer.drop(['ID'],axis=1,inplace=True) summer.drop(['Name'],axis=1,inplace=True) summer.drop(['index'],axis=1,inplace=True)
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summer
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#created a column for encoded team and encoded events in numerical form in original dataset summer['Team_encode']=le.fit_transform(summer['Team']) summer['Event_encode']=le.fit_transform(summer['Event'])
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#storing the team names and corresponding encoded numerical values into a new csv file after sorting them according to team name TeamKeys=summer[['Team','Team_encode']].copy() TeamKeys.drop_duplicates(subset="Team",inplace=True) TeamKeys.to_csv("keystoteam.csv")
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TeamKeys.head(4)
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#storing event names and corresponding encoded numerical values into a new csv file after sorting them according to the event name EventKeys=summer[['Event','Event_encode']].copy() EventKeys.drop_duplicates(subset="Event",inplace=True) EventKeys.to_csv("keystoevent.csv")
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EventKeys.head(4)
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summer
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summer.drop(['Event'],axis=1,inplace=True) summer.drop(['Team'],axis=1,inplace=True)
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summer
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y=summer['Medal']
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y
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x=summer.drop("Medal",axis=1)
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x
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X_train, X_test, Y_train, Y_test = train_test_split(x,y,test_size=0.30, random_state=99)
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x
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y
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X_test
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Y_test
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#ALGORITHM 1 LOGISTIC REGRESSION
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lr=LogisticRegression()
lr.fit(X_train,Y_train)
Y_pred=lr.predict(X_test)
sk_report=classification_report(digits=6,y_true=Y_test,y_pred=Y_pred) print("Accuracy",round(accuracy_score(Y_pred,Y_test)*100,2)) print(sk_report) print(pd.crosstab(Y_test,Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True))
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#ALGORITHM 2 DECESSION TREE
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decision_tree = DecisionTreeClassifier() decision_tree.fit(X_train, Y_train) Y_pred = decision_tree.predict(X_test) acc_decision_tree1 = round(decision_tree.score(X_test, Y_test) * 100, 2) sk_report = classification_report(digits=6, y_true=Y_test, y_pred=Y_pred) print("Accuracy", acc_decision_tree1) print(sk_report)
Confusion Matrix
print(pd.crosstab(Y_test, Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True))
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#ALGORITHM 3 RANDOM FOREST
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random_forest = RandomForestClassifier(n_estimators=200) random_forest.fit(X_train,Y_train) Y_pred = random_forest.predict(X_test) random_forest.score(X_test, Y_test) acc_random_forest1=round(random_forest.score(X_test, Y_test)*100,2) k_report = classification_report( digits=6, y_true=Y_test, y_pred=Y_pred) print("Accuracy" , acc_random_forest1) print(sk_report) pd.crosstab(Y_test, Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True)
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x.sample(5)
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y.sample(5)
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summer.sample(4)
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random_forest.predict([[1,19.0,173.0,70.0,87,163]])
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import pickle from joblib import dump,load
dump(random_forest,'olympics_model.pkl') model_file = open(r"Projects\Olympics\olympics_model1.pkl","wb") pickle.dump(random_forest,model_file)