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import streamlit as st
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.preprocessing import OneHotEncoder
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from ucimlrepo import fetch_ucirepo
# Page configuration
st.set_page_config(
page_title="Car Evaluation Analysis",
page_icon="🚗",
layout="wide"
)
# Title and introduction
st.title("🚗 Car Evaluation Analysis Dashboard")
st.markdown("""
This dashboard analyzes car evaluation data using different machine learning models.
The dataset includes various car attributes and their evaluation classifications.
""")
# Load and prepare data
@st.cache_data
def load_data():
car_evaluation = fetch_ucirepo(id=19)
X, y = car_evaluation.data.features, car_evaluation.data.targets
df = pd.concat([X, y], axis=1)
return df, X, y
df, X, y = load_data()
# Sidebar
st.sidebar.header("Navigation")
page = st.sidebar.radio("Go to", ["Data Overview", "Exploratory Analysis", "Model Training", "Model Comparison"])
# Data Overview Page
if page == "Data Overview":
st.header("Dataset Overview")
# Display metrics in cards
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric(
label="Total Records",
value=f"{len(df):,}"
)
with col2:
st.metric(
label="Features",
value=len(df.columns) - 1
)
with col3:
st.metric(
label="Target Classes",
value=len(df['class'].unique())
)
with col4:
st.metric(
label="Missing Values",
value=df.isnull().sum().sum()
)
st.write("")
# Sample Data
st.subheader("Sample Data")
st.dataframe(
df.head(),
use_container_width=True,
height=230
)
# Target Class Distribution
st.subheader("Target Class Distribution")
col1, col2 = st.columns([2, 1])
with col1:
fig, ax = plt.subplots(figsize=(10, 6))
sns.countplot(data=df, x='class', palette='viridis')
plt.title('Distribution of Car Evaluations')
st.pyplot(fig)
with col2:
st.write("")
st.write("")
class_distribution = df['class'].value_counts()
for class_name, count in class_distribution.items():
st.metric(
label=class_name,
value=count
)
# Exploratory Analysis Page
elif page == "Exploratory Analysis":
st.header("Exploratory Data Analysis")
# Feature Distribution
st.subheader("Feature Distributions")
feature_to_plot = st.selectbox("Select Feature", df.columns[:-1])
fig, ax = plt.subplots(figsize=(10, 6))
sns.countplot(data=df, x=feature_to_plot, palette='coolwarm')
plt.title(f'Distribution of {feature_to_plot}')
plt.xticks(rotation=45)
st.pyplot(fig)
# Feature vs Target
st.subheader("Feature vs Target Class")
fig, ax = plt.subplots(figsize=(12, 6))
sns.countplot(data=df, x=feature_to_plot, hue='class', palette='Set2')
plt.title(f'{feature_to_plot} Distribution by Class')
plt.xticks(rotation=45)
st.pyplot(fig)
# Correlation Heatmap
st.subheader("Correlation Heatmap")
encoded_df = pd.get_dummies(df, drop_first=True)
fig, ax = plt.subplots(figsize=(12, 8))
sns.heatmap(encoded_df.corr(), annot=True, fmt='.2f', cmap='coolwarm')
plt.title('Correlation Heatmap of Encoded Features')
st.pyplot(fig)
# Model Training Page
elif page == "Model Training":
st.header("Model Training and Evaluation")
# Data preprocessing
encoder = OneHotEncoder(sparse_output=False)
X_encoded = encoder.fit_transform(X)
y_encoded = y.values.ravel()
# Train-test split
test_size = st.slider("Select Test Size", 0.1, 0.4, 0.2, 0.05)
X_train, X_test, y_train, y_test = train_test_split(
X_encoded, y_encoded, test_size=test_size, random_state=42
)
# Model selection
model_choice = st.selectbox(
"Select Model",
["Support Vector Machine", "Random Forest", "Logistic Regression"]
)
if st.button("Train Model"):
with st.spinner("Training model..."):
if model_choice == "Support Vector Machine":
model = SVC(kernel='linear', random_state=42)
elif model_choice == "Random Forest":
model = RandomForestClassifier(n_estimators=100, random_state=42)
else:
model = LogisticRegression(max_iter=500, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# Display results
col1, col2 = st.columns(2)
with col1:
st.subheader("Model Performance")
accuracy = accuracy_score(y_test, y_pred)
st.metric(label="Accuracy", value=f"{accuracy:.4f}")
st.text("Classification Report:")
st.text(classification_report(y_test, y_pred))
with col2:
st.subheader("Confusion Matrix")
fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(
confusion_matrix(y_test, y_pred),
annot=True,
fmt='d',
cmap='Blues',
xticklabels=np.unique(y_test),
yticklabels=np.unique(y_test)
)
plt.title(f'{model_choice} Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('Actual')
st.pyplot(fig)
# Feature importance for Random Forest
if model_choice == "Random Forest":
st.subheader("Feature Importance")
feature_importance = pd.DataFrame({
'feature': encoder.get_feature_names_out(),
'importance': model.feature_importances_
})
feature_importance = feature_importance.sort_values(
'importance', ascending=False
).head(10)
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(
data=feature_importance,
x='importance',
y='feature'
)
plt.title('Top 10 Most Important Features')
st.pyplot(fig)
# Model Comparison Page
else:
st.header("Model Comparison")
if st.button("Compare All Models"):
with st.spinner("Training all models..."):
# Data preprocessing
encoder = OneHotEncoder(sparse_output=False)
X_encoded = encoder.fit_transform(X)
y_encoded = y.values.ravel()
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
X_encoded, y_encoded, test_size=0.2, random_state=42
)
# Train all models
models = {
"SVM": SVC(kernel='linear', random_state=42),
"Random Forest": RandomForestClassifier(n_estimators=100, random_state=42),
"Logistic Regression": LogisticRegression(max_iter=500, random_state=42)
}
results = {}
for name, model in models.items():
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
results[name] = {
'accuracy': accuracy_score(y_test, y_pred),
'predictions': y_pred
}
# Display comparison results
st.subheader("Accuracy Comparison")
accuracy_df = pd.DataFrame({
'Model': list(results.keys()),
'Accuracy': [results[model]['accuracy'] for model in results.keys()]
})
col1, col2 = st.columns(2)
with col1:
st.dataframe(accuracy_df)
with col2:
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(
data=accuracy_df,
x='Model',
y='Accuracy',
palette='viridis'
)
plt.title('Model Accuracy Comparison')
plt.ylim(0, 1)
st.pyplot(fig)
# Detailed model comparison
st.subheader("Detailed Model Performance")
for name in results.keys():
st.write(f"\n{name}:")
st.text(classification_report(y_test, results[name]['predictions']))
fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(
confusion_matrix(y_test, results[name]['predictions']),
annot=True,
fmt='d',
cmap='Blues',
xticklabels=np.unique(y_test),
yticklabels=np.unique(y_test)
)
plt.title(f'{name} Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('Actual')
st.pyplot(fig)
# Footer
st.markdown("""
---
Created with ❤️ using Streamlit
""") |