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EDA.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+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)
+
+
+

business_problem.py

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+import streamlit as st 
+
+import streamlit as st
+
+st.header("🚗 Brake System Fault Detection")
+
+
+
+feature_desc = {
+    'Brake_Pressure': "Pressure applied to the brake pedal.",
+    'Pad_Wear_Level': "Indicates the wear level of brake pads.",
+    'ABS_Status': "1 if Anti-lock Braking System is active, else 0.",
+    'Wheel_Speed_FL': "Speed of the front-left wheel.",
+    'Wheel_Speed_FR': "Speed of the front-right wheel.",
+    'Wheel_Speed_RL': "Speed of the rear-left wheel.",
+    'Wheel_Speed_RR': "Speed of the rear-right wheel.",
+    'Fluid_Temperature': "Temperature of the brake fluid.",
+    'Pedal_Position': "How far the brake pedal is pressed."
+}
+
+selected = st.selectbox("Select a feature to understand:", list(feature_desc.keys()))
+st.info(f"📘 **{selected}**: {feature_desc[selected]}")
+
+
+
+
+# 🎯 Goal
+st.markdown("### 🎯 Goal")
+st.markdown("""
+Build a data-driven model that detects braking system faults using sensor data such as brake pressure, wheel speeds, fluid temperature, and pedal position.
+""")
+
+# 💼 Business Objective
+st.markdown("### 📌 Business Objective")
+st.markdown("""
+- Detect faults early to reduce vehicle failure risks.
+- Analyze sensor behavior during fault vs non-fault conditions.
+- Support preventive maintenance using historical data patterns.
+""")
+st.markdown("### 📊 Data Understanding")
+
+st.markdown("""
+The dataset contains **real-time sensor readings** collected from a vehicle's braking system to detect faults. Below are the details of each feature:
+
+#### 🔢 Numerical Features:
+
+- **Brake_Pressure**: Pressure applied to the braking system (in bar or psi).
+- **Pad_Wear_Level**: Indicates how much the brake pads have worn down (in % or mm).
+- **Wheel_Speed_FL**: Speed of the **front-left** wheel (in km/h).
+- **Wheel_Speed_FR**: Speed of the **front-right** wheel (in km/h).
+- **Wheel_Speed_RL**: Speed of the **rear-left** wheel (in km/h).
+- **Wheel_Speed_RR**: Speed of the **rear-right** wheel (in km/h).
+- **Fluid_Temperature**: Temperature of the brake fluid (in °C).
+- **Pedal_Position**: Position of the brake pedal, indicating how far it is pressed (0–100%).
+
+#### 🔠 Categorical Feature:
+
+- **ABS_Status**: Anti-lock Braking System status —  
+  - `1`: Active  
+  - `0`: Inactive
+
+#### 🎯 Target Variable:
+
+- **Fault**:
+  - `0`: Normal (No issue detected)  
+  - `1`: Fault Detected (Brake system problem)
+""")