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# Import required libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset
data = pd.read_csv('test.csv')
# Data Preprocessing
# Filling missing values using the dictionary format to avoid FutureWarning
data.fillna({'Age': data['Age'].median(), 'Fare': data['Fare'].median(), 'Embarked': data['Embarked'].mode()[0]}, inplace=True)
# Encoding categorical variables
label_encoder_sex = LabelEncoder()
label_encoder_embarked = LabelEncoder()
data['Sex'] = label_encoder_sex.fit_transform(data['Sex'])
data['Embarked'] = label_encoder_embarked.fit_transform(data['Embarked'])
# Feature Engineering
# Selecting relevant features (Pclass, Sex, Age, SibSp, Parch, Fare)
# Target is the 'Embarked' column
X = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]
y = data['Embarked']
# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Training the Naive Bayes Classifier
nb_classifier = MultinomialNB()
nb_classifier.fit(X_train, y_train)
# Making predictions
y_pred = nb_classifier.predict(X_test)
# Evaluating the model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
# Outputting results
print(f"Accuracy: {accuracy}")
print("\nConfusion Matrix:\n", conf_matrix)
print("\nClassification Report:\n", class_report)
Accuracy: 0.3968253968253968
Confusion Matrix:
[[ 8 8 11]
[ 0 13 1]
[12 44 29]]
Classification Report:
precision recall f1-score support
0 0.40 0.30 0.34 27
1 0.20 0.93 0.33 14
2 0.71 0.34 0.46 85
accuracy 0.40 126
macro avg 0.44 0.52 0.38 126
weighted avg 0.59 0.40 0.42 126
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# Display a concise summary of the dataset
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 418 entries, 0 to 417 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 418 non-null int64 1 Pclass 418 non-null int64 2 Name 418 non-null object 3 Sex 418 non-null int64 4 Age 418 non-null float64 5 SibSp 418 non-null int64 6 Parch 418 non-null int64 7 Ticket 418 non-null object 8 Fare 418 non-null float64 9 Cabin 91 non-null object 10 Embarked 418 non-null int64 dtypes: float64(2), int64(6), object(3) memory usage: 36.0+ KB
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import joblib
joblib.dump(nb_classifier, 'naive_bayes_classifier_model.pkl')
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['naive_bayes_classifier_model.pkl']
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# Visualizing the distribution of Age
plt.figure(figsize=(8, 6))
sns.histplot(data['Age'], bins=30, kde=True, color='skyblue')
plt.title('Distribution of Age')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.show()
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# Visualizing the distribution of Fare
plt.figure(figsize=(8, 6))
sns.histplot(data['Fare'], bins=30, kde=True, color='green')
plt.title('Distribution of Fare')
plt.xlabel('Fare')
plt.ylabel('Frequency')
plt.show()
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# Visualizing the count of Embarked locations
plt.figure(figsize=(8, 6))
sns.countplot(data=data, x='Embarked', order=data['Embarked'].value_counts().index)
plt.title('Count of Passengers by Embarked Location')
plt.xlabel('Embarked Location')
plt.ylabel('Count')
plt.show()
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# Visualizing the correlation matrix
plt.figure(figsize=(8, 6))
correlation_matrix = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Matrix of Selected Features')
plt.show()
