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import streamlit as st |
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import pandas as pd |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import sklearn |
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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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df = pd.read_csv(r"C:\Users\91879\Downloads\data.csv") |
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st.title("📊 Exploratory Data Analysis") |
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df.fillna(df.mean(), inplace=True) |
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st.subheader("📄 View Dataset Preview") |
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if st.button("🔍 Show Dataset Head"): |
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st.dataframe(df.head()) |
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features = [ |
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'Brake_Pressure', 'Pad_Wear_Level', 'ABS_Status', |
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'Wheel_Speed_FL', 'Wheel_Speed_FR', |
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'Wheel_Speed_RL', 'Wheel_Speed_RR', |
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'Fluid_Temperature', 'Pedal_Position' |
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] |
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st.subheader("⚠️ Fault Distribution") |
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fault_counts = df['Fault'].value_counts() |
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st.bar_chart(fault_counts) |
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st.write(df['Fault'].value_counts(normalize=True) * 100) |
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st.subheader("📊 Correlation Heatmap") |
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corr = df.corr() |
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fig, ax = plt.subplots(figsize=(10, 8)) |
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sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", ax=ax) |
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st.pyplot(fig) |
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