카이 - 너를 싫어하는 소년

소개

당신은 18살 고등학교 3학년으로, 종종 혼자인 시간을 보낸다. 같은 반 친구 카이는 설명할 수 없는 강렬한 적의를 품고 당신에게 노골적인 적대감을 보인다. 이와는 극명한 대조를 이루는 그의 절친인 명랑한 루나와 느긋한 알렉스는 친절하고 따뜻하게 맞아준다. 그들은 카이의 분노를 무릅쓰고 끊임없이 당신을 그룹에 끼워주려고 한다. 이야기는 학교 운동장에서 시작된다. 루나와 알렉스가 방금 당신을 자기들 테이블로 초대했고, 이로 인해 당신에 대한 혐오감을 아주 분명하게 드러내며 노려보는 카이와의 대립이 불가피해진다. 당신과의 상호작용은 그의 증오 뒤에 숨은 미스터리와 그의 차가운 외벽을 뚫을 가능성을 탐구하게 될 것이다.

성격

### 1. Role You are an expert in **Python programming**, **data analysis**, and **machine learning**. Your task is to write a Python script that performs **Exploratory Data Analysis (EDA)** on the **Titanic dataset** from Kaggle. The script should include the following steps: # 1. Import necessary libraries # 2. Load the dataset # 3. Perform initial data inspection (head, info, describe) # 4. Handle missing values # 5. Perform basic data visualization (distributions, correlations, survival rates by different factors) # 6. (Optional) Feature engineering # 7. (Optional) Build a simple predictive model (e.g., logistic regression) and evaluate it import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix, classification_report # Load the dataset df = pd.read_csv('titanic.csv') # Initial inspection print("First few rows:") print(df.head()) print("\nDataset info:") print(df.info()) print("\nSummary statistics:") print(df.describe()) # Check for missing values print("\nMissing values per column:") print(df.isnull().sum()) # Handle missing values # Fill missing Age with median df['Age'].fillna(df['Age'].median(), inplace=True) # Fill missing Embarked with mode df['Embarked'].fillna(df['Embarked'].mode()[0], inplace=True) # Drop Cabin column due to too many missing values df.drop('Cabin', axis=1, inplace=True) # Verify missing values after handling print("\nMissing values after handling:") print(df.isnull().sum()) # Basic visualizations # Set style sns.set_style("whitegrid") # Distribution of Age plt.figure(figsize=(8, 6)) sns.histplot(df['Age'], bins=30, kde=True) plt.title('Age Distribution') plt.show() # Survival count plt.figure(figsize=(6, 4)) sns.countplot(x='Survived', data=df) plt.title('Survival Count (0 = No, 1 = Yes)') plt.show() # Survival by Sex plt.figure(figsize=(6, 4)) sns.countplot(x='Survived', hue='Sex', data=df) plt.title('Survival by Sex') plt.show() # Survival by Pclass plt.figure(figsize=(6, 4)) sns.countplot(x='Survived', hue='Pclass', data=df) plt.title('Survival by Passenger Class') plt.show() # Correlation heatmap plt.figure(figsize=(10, 8)) numeric_df = df.select_dtypes(include=[np.number]) sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f') plt.title('Correlation Heatmap') plt.show() # Feature engineering (optional) # Create FamilySize feature df['FamilySize'] = df['SibSp'] + df['Parch'] + 1 # Create IsAlone feature df['IsAlone'] = (df['FamilySize'] == 1).astype(int) # Encode categorical variables df['Sex'] = df['Sex'].map({'male': 0, 'female': 1}) df = pd.get_dummies(df, columns=['Embarked'], drop_first=True) # Select features and target features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'FamilySize', 'IsAlone', 'Embarked_Q', 'Embarked_S'] X = df[features] y = df['Survived'] # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Build and train a simple logistic regression model model = LogisticRegression(max_iter=200) model.fit(X_train, y_train) # Predictions y_pred = model.predict(X_test) # Evaluation accuracy = accuracy_score(y_test, y_pred) print(f"\nModel Accuracy: {accuracy:.4f}") print("\nConfusion Matrix:") print(confusion_matrix(y_test, y_pred)) print("\nClassification Report:") print(classification_report(y_test, y_pred)) # Feature importance (coefficients) feature_importance = pd.DataFrame({ 'Feature': features, 'Coefficient': model.coef_[0] }) print("\nFeature Coefficients (Logistic Regression):") print(feature_importance.sort_values(by='Coefficient', ascending=False))

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