Titanic
2) Create a sns.barplot using x = "Pclass" and Y="Survived" and data=train in a tuple
sns.barplot(x="Pclass", y="Survived", data=train)
1) Import seaborn as sns 2) Create a sns.barplot using x = "Sex" and Y="Survived" and data=train_data in a tuple
sns.barplot(x="Sex", y="Survived", data=train_data)
Create a sns.barplot using x = "Sibsp" and Y="Survived" and data=train in a tuple
sns.barplot(x="SibSp", y="Survived", data=train)
# KNN or k-Nearest Neighbors from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() knn.fit(x_train, y_train) y_pred = knn.predict(x_val) acc_knn = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_knn)
77.66
# Gaussian Naive Bayes from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score gaussian = GaussianNB() gaussian.fit(x_train, y_train) y_pred = gaussian.predict(x_val) acc_gaussian = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_gaussian)
78.68
# Linear SVC from sklearn.svm import LinearSVC linear_svc = LinearSVC() linear_svc.fit(x_train, y_train) y_pred = linear_svc.predict(x_val) acc_linear_svc = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_linear_svc)
78.68
# Logistic Regression from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() logreg.fit(x_train, y_train) y_pred = logreg.predict(x_val) acc_logreg = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_logreg)
79.19
# Perceptron from sklearn.linear_model import Perceptron perceptron = Perceptron() perceptron.fit(x_train, y_train) y_pred = perceptron.predict(x_val) acc_perceptron = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_perceptron)
79.19
# Stochastic Gradient Descent from sklearn.linear_model import SGDClassifier sgd = SGDClassifier() sgd.fit(x_train, y_train) y_pred = sgd.predict(x_val) acc_sgd = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_sgd)
80.2
#Decision Tree from sklearn.tree import DecisionTreeClassifier decisiontree = DecisionTreeClassifier() decisiontree.fit(x_train, y_train) y_pred = decisiontree.predict(x_val) acc_decisiontree = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_decisiontree)
80.71
# Random Forest from sklearn.ensemble import RandomForestClassifier randomforest = RandomForestClassifier() randomforest.fit(x_train, y_train) y_pred = randomforest.predict(x_val) acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_randomforest)
81.22
# Support Vector Machines from sklearn.svm import SVC svc = SVC() svc.fit(x_train, y_train) y_pred = svc.predict(x_val) acc_svc = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_svc)
82.74
# Gradient Boosting Classifier from sklearn.ensemble import GradientBoostingClassifier gbk = GradientBoostingClassifier() gbk.fit(x_train, y_train) y_pred = gbk.predict(x_val) acc_gbk = round(accuracy_score(y_pred, y_val) * 100, 2) print(acc_gbk)
84.77
