EDA.py

import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import streamlit as st
import pandas as pd
import numpy as np

# Load the dataset
df = pd.read_csv(r"C:\Users\91879\Downloads\data.csv")

st.title("📊 Exploratory Data Analysis")


    # Fill missing values
df.fillna(df.mean(), inplace=True)
st.subheader("📄 View Dataset Preview")

if st.button("🔍 Show Dataset Head"):
    st.dataframe(df.head())


features = [
            'Brake_Pressure', 'Pad_Wear_Level', 'ABS_Status',
            'Wheel_Speed_FL', 'Wheel_Speed_FR',
            'Wheel_Speed_RL', 'Wheel_Speed_RR',
            'Fluid_Temperature', 'Pedal_Position'
        ]

st.subheader("⚠️ Fault Distribution")
fault_counts = df['Fault'].value_counts()
st.bar_chart(fault_counts)
st.write(df['Fault'].value_counts(normalize=True) * 100)



st.subheader("📊 Correlation Heatmap")
corr = df.corr()
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", ax=ax)
st.pyplot(fig)