# data analysis and wrangling import pandas as pd import numpy as np import random as rnd from collections import Counter # visualization import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import StandardScaler # machine learning from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC, LinearSVC from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import Perceptron from sklearn.linear_model import SGDClassifier from sklearn.tree import DecisionTreeClassifier # validation from sklearn.metrics import accuracy_score

encoder = LabelEncoder() scaler = StandardScaler()

train_data = pd.read_csv("train.csv") test_data = pd.read_csv("test.csv") fake_submit = pd.read_csv("gender_submission.csv") combine = [train_data, test_data]

# Outlier detection def detect_outliers(df,n,features): """ Takes a dataframe df of features and returns a list of the indices corresponding to the observations containing more than n outliers according to the Tukey method. """ outlier_indices = [] # iterate over features(columns) for col in features: # 1st quartile (25%) Q1 = np.percentile(df[col], 25) # 3rd quartile (75%) Q3 = np.percentile(df[col],75) # Interquartile range (IQR) IQR = Q3 - Q1 # outlier step outlier_step = 1.5 * IQR # Determine a list of indices of outliers for feature col outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index # append the found outlier indices for col to the list of outlier indices outlier_indices.extend(outlier_list_col) # select observations containing more than 2 outliers outlier_indices = Counter(outlier_indices) multiple_outliers = list( k for k, v in outlier_indices.items() if v > n ) return multiple_outliers # detect outliers from Age, SibSp , Parch and Fare Outliers_to_drop = detect_outliers(train_data,2,["Age","SibSp","Parch","Fare"]) train_data = train_data.drop(Outliers_to_drop, axis = 0).reset_index(drop=True)

train_data.head()

train_data.info()

train_data.describe(percentiles=[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]) # 1% la sibsp > 5 # 1% age > 65 ; 50% thuoc nhom [20,40] # 38% survived -> 62% dead

train_data.describe(include=["O"])

train_data.corr().sort_values(by="Survived")

## Make assumtions ### Complete : Age, Embarked ### Convert: Sex, Ticket, Embarked ### Creating: Age Bands, Fare range, SibSp&Parch ###

train_data[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)

train_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)

train_data[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)

train_data[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)

train_data[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)

## Age vs Survived g = sns.FacetGrid(train_data, col='Survived') g.map(plt.hist, 'Age', bins=20)

## Age vs Survived | Pclass # grid = sns.FacetGrid(train_data, col='Pclass', hue='Survived') grid = sns.FacetGrid(train_data, col='Survived', row='Pclass', height=2.2, aspect=1.6) grid.map(plt.hist, 'Age', alpha=.5, bins=20) grid.add_legend();

## Pclass vs Sex vs Survived | Embarked grid = sns.FacetGrid(train_data, row='Embarked', height=2.2, aspect=1.6) grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep') grid.add_legend()

## Fare vs Survived | Embarked vs Sex grid = sns.FacetGrid(train_data, row='Embarked', col='Sex', height=2.2, aspect=1.6) grid.map(sns.barplot, 'Survived', 'Fare', alpha=.5, ci=None) grid.add_legend()

train_data = train_data.drop(["Cabin", "PassengerId", "Name"], axis = 1) test_data = test_data.drop(["Cabin", "Name"], axis = 1) combine = [train_data, test_data]

for dataset in combine: dataset["Sex"] = encoder.fit_transform(dataset["Sex"])

freq_port = train_data["Embarked"].dropna().mode() for dataset in combine: dataset["Embarked"] = dataset["Embarked"].fillna(freq_port[0]) dataset["Fare"] = dataset["Fare"].fillna(dataset["Fare"].mean())

for dataset in combine: dataset["Embarked"] = encoder.fit_transform(dataset["Embarked"])

for dataset in combine: dataset["Family Size"] = dataset["SibSp"] + dataset["Parch"] + 1 for dataset in combine: dataset["Alone"] = 0 dataset.loc[dataset["Family Size"] == 1, "Alone"] = 1

train_data = train_data.drop(["Family Size", "SibSp", "Parch"], axis = 1) test_data = test_data.drop(["Family Size", "SibSp", "Parch"], axis = 1) combine = [train_data, test_data]

grid = sns.FacetGrid(train_data, col='Pclass', row='Alone', height=2.2, aspect=1.6) grid.map(plt.hist, 'Age', alpha=.5, bins=20) grid.add_legend();

age_guess = np.zeros((train_data["Alone"].unique().size, train_data["Pclass"].unique().size))

age_guess.shape

for dataset in combine: for i in range(age_guess.shape[0]): for j in range(age_guess.shape[1]): guess_df = dataset.loc[(dataset["Alone"] == i) & (dataset["Pclass"] == j + 1), "Age"].dropna() guess = guess_df.mean() age_guess[i,j] = guess for i in range(age_guess.shape[0]): for j in range(age_guess.shape[1]): dataset.loc[(dataset["Age"].isnull()) & (dataset["Alone"] == i) & (dataset["Pclass"] == j + 1), "Age"] = age_guess[i,j] dataset["Age"] = np.round(dataset["Age"])

grid = sns.FacetGrid(train_data, col='Survived', height=2.2, aspect=1.6) grid.map(plt.hist, 'Age', alpha=.5, bins=20) grid.add_legend();

train_data["AgeBand"] = pd.cut(train_data["Age"], 5) train_data[["AgeBand", "Survived"]].groupby("AgeBand", as_index=False).mean().sort_values(by="AgeBand")

for dataset in combine: dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0 dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1 dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2 dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3 dataset.loc[ dataset['Age'] > 64, 'Age']

train_data = train_data.drop("Ticket", axis=1) test_data = test_data.drop("Ticket", axis=1) combine = [train_data, test_data]

train_data['FareBand'] = pd.qcut(train_data['Fare'], 4) train_data[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)

for dataset in combine: dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0 dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1 dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2 dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3

train_data = train_data.drop(["AgeBand", "FareBand"], axis = 1)

X = train_data.drop("Survived", axis=1) y = train_data["Survived"] X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) X_test = test_data.drop("PassengerId", axis=1)

logreg = LogisticRegression(solver="lbfgs") logreg.fit(X_train, y_train) log_pred = logreg.predict(X_val) print("Accuracy score:", accuracy_score(log_pred, y_val)) print("Train Accuracy: ", accuracy_score(logreg.predict(X_train), y_train))

svc = SVC(kernel="rbf", C=0.9, gamma=0.2) svc.fit(X_train, y_train) svc_pred = svc.predict(X_val) print("Val Accuracy: ", accuracy_score(svc_pred, y_val)) print("Train Accuracy: ", accuracy_score(svc.predict(X_train), y_train))

knn = KNeighborsClassifier(n_neighbors=3) knn.fit(X_train, y_train) knn_pred = knn.predict(X_val) print("Val Accuracy: ", accuracy_score(knn_pred, y_val)) print("Train Accuracy: ", accuracy_score(knn.predict(X_train), y_train))

tree = DecisionTreeClassifier(max_depth=3, criterion="entropy") tree.fit(X_train, y_train) tree_pred = tree.predict(X_val) print("Val Accuracy: ", accuracy_score(tree_pred, y_val)) print("Train Accuracy: ", accuracy_score(tree.predict(X_train), y_train))

forest = RandomForestClassifier(n_estimators=50, criterion="entropy", max_depth=5) forest.fit(X_train, y_train) forest_pred = forest.predict(X_val) print("Val Accuracy: ", accuracy_score(forest_pred, y_val)) print("Train Accuracy: ", accuracy_score(forest.predict(X_train), y_train))

y_test = forest.predict(X_test) submission = pd.DataFrame({ "PassengerId": test_data["PassengerId"], "Survived": y_test }) submission.to_csv('submission.csv', index=False)