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import pandas as pd
import gradio as gr
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
# Load and preprocess the dataset
data = pd.read_csv('data.csv')
# Preprocessing
data['Age'] = data['Age'].fillna(data['Age'].median())
data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])
data['Fare'] = pd.to_numeric(data['Fare'], errors='coerce')
data['Fare'] = data['Fare'].fillna(data['Fare'].median())
label_encoder = LabelEncoder()
data['Gender'] = label_encoder.fit_transform(data['Gender'])
data['Embarked'] = label_encoder.fit_transform(data['Embarked'])
data.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
# Feature selection
features = ['Pclass', 'Gender', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
X = data[features]
y = data['Survived']
# Train the model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
# Gradio interface function
def predict_survival(Pclass, Gender, Age, SibSp, Parch, Fare, Embarked):
# Handle missing or invalid inputs
if not Gender:
return "⚠️ Error: Please select a Gender."
if not Embarked:
return "⚠️ Error: Please select a Port of Embarkation."
# Encode Gender and Embarked
Gender_encoded = 1 if Gender.lower() == 'female' else 0
Embarked_encoded = {'s': 0, 'c': 1, 'q': 2}.get(Embarked.lower(), 0)
# Create input DataFrame
input_data = pd.DataFrame([[Pclass, Gender_encoded, Age, SibSp, Parch, Fare, Embarked_encoded]],
columns=features)
# Predict
prediction = model.predict(input_data)
return "✅ Survived" if prediction[0] == 1 else "❌ Did Not Survive"
# Gradio inputs and outputs
inputs = [
gr.Slider(1, 3, step=1, label="Passenger Class (Pclass)"),
gr.Radio(["Male", "Female"], label="Gender"),
gr.Slider(0, 80, step=1, label="Age (in years)"),
gr.Slider(0, 10, step=1, label="Siblings/Spouses (SibSp)"),
gr.Slider(0, 10, step=1, label="Parents/Children (Parch)"),
gr.Slider(0, 500, step=1, label="Ticket Fare (in $)"),
gr.Radio(["S (Southampton)", "C (Cherbourg)", "Q (Queenstown)"], label="Port of Embarkation (Embarked)")
]
outputs = gr.Textbox(label="Prediction or Error Message")
# Launch Gradio interface
gr.Interface(fn=predict_survival, inputs=inputs, outputs=outputs, title="Titanic Survival Predictor By Ozan").launch()
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