df['age']=df['age'].fillna(df['age'].mean())

# Importing Libraries import pandas as pd import numpy as np import seaborn as sns# Loading DataSet df=sns.load_dataset('titanic') df['age']=df['age'].fillna(df['age'].mean()) x=df[['age','fare','pclass','parch','sex','sibsp']] y=df['survived'] x=pd.get_dummies(x,columns=['sex'])# Importing ML Algorithm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.75, random_state=42)# Random ForestPrameter param_cv={'n_estimators':[10,50 ,100], 'min_samples_split':[2,3,4], 'max_depth':[3,5,7, None], 'max_features': ['log2','sqrt']}# Decision Tree Parameter param_cvv={ 'max_depth':[3,5,7,None],'min_samples_split':[ 2,3,4]}# KNN Neighbour Parameter param_cvvv={'n_neighbors':[5,7],'weights':['uniform','distance']}models = [(RandomForestClassifier(),param_cv,'Random Forest Classifier'), (DecisionTreeClassifier(),param_cvv,'Decision Tree Classifier'), (KNeighborsClassifier(),param_cvvv,'KNeighbour')] # model_names = ['Random Forest Classifier','Decision Tree Classifer','KNN Classifier'] for model,param_grid,model_names in models: grid_search=GridSearchCV(model,param_grid,cv=5,scoring='accuracy') grid_search.fit(x,y) print(f"Model: {model_names}") print('Best Parameters are: ',grid_search.best_params_) print("Best Score:",grid_search.best_score_*100) best_model=grid_search.best_estimator_ y_predict=best_model.predict(x_test) accurac=accuracy_score(y_test,y_predict) print('Accuracy Score for ',model_names,'is',accurac) print("n")

Thanks for so comprehensive Leactures

X = pd.get_dummies(X, columns=['sex'])

df['age'].fillna(df['age'].mean(),inplace=True)

age columns main nan values hain

how we share my notebook in github

github ka link please share kar dain

X.age.fillna(value = X['age'].mean(), inplace=True)