Titanic
Mon 30 June 2025
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Note: you may need to restart the kernel to use updated packages.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
titanic = sns.load_dataset("titanic")
titanic.head()
| survived | pclass | sex | age | sibsp | parch | fare | embarked | class | who | adult_male | deck | embark_town | alive | alone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 3 | male | 22.0 | 1 | 0 | 7.2500 | S | Third | man | True | NaN | Southampton | no | False |
| 1 | 1 | 1 | female | 38.0 | 1 | 0 | 71.2833 | C | First | woman | False | C | Cherbourg | yes | False |
| 2 | 1 | 3 | female | 26.0 | 0 | 0 | 7.9250 | S | Third | woman | False | NaN | Southampton | yes | True |
| 3 | 1 | 1 | female | 35.0 | 1 | 0 | 53.1000 | S | First | woman | False | C | Southampton | yes | False |
| 4 | 0 | 3 | male | 35.0 | 0 | 0 | 8.0500 | S | Third | man | True | NaN | Southampton | no | True |
titanic.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 class 891 non-null category
9 who 891 non-null object
10 adult_male 891 non-null bool
11 deck 203 non-null category
12 embark_town 889 non-null object
13 alive 891 non-null object
14 alone 891 non-null bool
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB
titanic.isnull().sum()
survived 0
pclass 0
sex 0
age 177
sibsp 0
parch 0
fare 0
embarked 2
class 0
who 0
adult_male 0
deck 688
embark_town 2
alive 0
alone 0
dtype: int64
# Fill missing age values with the median
titanic["age"] = titanic["age"].fillna(titanic["age"].median())
# Check if there are still any missing values
titanic["age"].isnull().sum()
np.int64(0)
# Fill missing embarked values with mode
titanic["embarked"] = titanic["embarked"].fillna(titanic["embarked"].mode()[0])
# Convert categorical columns into numerical using one-hot encoding
titanic_encoded = pd.get_dummies(titanic, columns=["sex", "embarked", "pclass"], drop_first=True)
titanic_encoded.head()
| survived | age | sibsp | parch | fare | class | who | adult_male | deck | embark_town | alive | alone | sex_male | embarked_Q | embarked_S | pclass_2 | pclass_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 22.0 | 1 | 0 | 7.2500 | Third | man | True | NaN | Southampton | no | False | True | False | True | False | True |
| 1 | 1 | 38.0 | 1 | 0 | 71.2833 | First | woman | False | C | Cherbourg | yes | False | False | False | False | False | False |
| 2 | 1 | 26.0 | 0 | 0 | 7.9250 | Third | woman | False | NaN | Southampton | yes | True | False | False | True | False | True |
| 3 | 1 | 35.0 | 1 | 0 | 53.1000 | First | woman | False | C | Southampton | yes | False | False | False | True | False | False |
| 4 | 0 | 35.0 | 0 | 0 | 8.0500 | Third | man | True | NaN | Southampton | no | True | True | False | True | False | True |
# Optional: Drop columns that are not useful for prediction
titanic = titanic.drop(columns=["who", "adult_male", "deck", "embark_town", "alive", "class", "alone"], errors='ignore')
# Fill missing values
titanic["age"] = titanic["age"].fillna(titanic["age"].median())
titanic["fare"] = titanic["fare"].fillna(titanic["fare"].median())
titanic["embarked"] = titanic["embarked"].fillna(titanic["embarked"].mode()[0])
# Convert categorical columns to numeric
titanic_encoded = pd.get_dummies(titanic, columns=["sex", "embarked", "pclass"], drop_first=True)
# Define features and label
X = titanic_encoded.drop("survived", axis=1)
y = titanic_encoded["survived"]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)
LogisticRegression(max_iter=200)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Parameters
| penalty | 'l2' | |
| dual | False | |
| tol | 0.0001 | |
| C | 1.0 | |
| fit_intercept | True | |
| intercept_scaling | 1 | |
| class_weight | None | |
| random_state | None | |
| solver | 'lbfgs' | |
| max_iter | 200 | |
| multi_class | 'deprecated' | |
| verbose | 0 | |
| warm_start | False | |
| n_jobs | None | |
| l1_ratio | None |
# Predict on the test set
y_pred = model.predict(X_test)
from sklearn.metrics import accuracy_score
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
Accuracy: 0.80
from sklearn.metrics import classification_report
# Detailed performance report
print(classification_report(y_test, y_pred))
precision recall f1-score support
0 0.81 0.86 0.83 105
1 0.78 0.72 0.75 74
accuracy 0.80 179
macro avg 0.80 0.79 0.79 179
weighted avg 0.80 0.80 0.80 179
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Plot confusion matrix
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()
# Show feature importance
coefficients = pd.Series(model.coef_[0], index=X.columns)
coefficients.sort_values().plot(kind='barh', figsize=(10,6), title="Feature Impact on Survival")
plt.tight_layout()
plt.show()
Score: 15
Category: basics