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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data.csv filter=lfs diff=lfs merge=lfs -text

data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:263d34a56e5bcc2ef097dca34f8255968b4f3137bedd7040f65608857255db09
+size 14799315

final.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import warnings
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+warnings.filterwarnings("ignore")
+
+# Load the dataset
+df = pd.read_csv(r"C:\Users\91879\Downloads\data.csv")
+df.fillna(df.mean(), inplace=True)
+
+# Create Tabs
+tab1, tab2, tab3 = st.tabs(["📘 Project Overview", "📊 EDA", "🔍 Fault Prediction"])
+
+# ----------------------------- TAB 1 ---------------------------------
+with tab1:
+    st.header("🚗 Brake System Fault Detection")
+    st.markdown("### 🧩 Business Problem")
+
+    st.markdown("""
+In the automotive industry, ensuring the safety and reliability of braking systems is **mission-critical**. Traditional brake inspections are typically **manual and reactive**, often identifying problems **only after they occur** or during scheduled maintenance.
+
+However, undetected faults in braking systems can lead to:
+- **Brake failure during operation**
+- **Reduced vehicle control**
+- **Increased risk of accidents**
+- **Expensive emergency repairs**
+
+Manufacturers and fleet managers need a **real-time fault detection system** using **sensor data** to:
+- Monitor brake system health continuously
+- **Predict faults proactively**
+- **Minimize vehicle downtime**
+- Enhance **safety, reliability, and cost-efficiency**
+""")
+
+
+    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.
+
+    #### 🔢 Numerical Features:
+    - **Brake_Pressure**
+    - **Pad_Wear_Level**
+    - **Wheel_Speed_FL**, **Wheel_Speed_FR**, **Wheel_Speed_RL**, **Wheel_Speed_RR**
+    - **Fluid_Temperature**
+    - **Pedal_Position**
+
+    #### 🔠 Categorical Feature:
+    - **ABS_Status**: `1` = Active, `0` = Inactive
+
+    #### 🎯 Target Variable:
+    - **Fault**: `1` = Fault Detected, `0` = No Fault
+    """)
+
+# ----------------------------- TAB 2 ---------------------------------
+with tab2:
+    st.title("📊 Exploratory Data Analysis")
+
+    st.subheader("📄 View Dataset Preview")
+    if st.button("🔍 Show Dataset Head"):
+        st.dataframe(df.head())
+
+    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)
+    
+    st.markdown("### 📉 Feature Distributions by Fault")
+    features = ['Brake_Pressure', 'Pad_Wear_Level', 'Wheel_Speed_FL', 'Wheel_Speed_FR',
+                'Wheel_Speed_RL', 'Wheel_Speed_RR', 'Fluid_Temperature', 'Pedal_Position']
+
+    for feature in features:
+        st.markdown(f"#### 🔍 {feature}")
+        fig, ax = plt.subplots()
+        sns.kdeplot(data=df, x=feature, hue="Fault", fill=True, common_norm=False, alpha=0.4, ax=ax)
+        st.pyplot(fig)
+
+    st.markdown("### 📦 Boxplots to Compare Fault vs Normal")
+    for feature in features:
+        st.markdown(f"#### 📦 {feature} vs Fault")
+        fig, ax = plt.subplots()
+        sns.boxplot(data=df, x='Fault', y=feature, palette="Set2", ax=ax)
+        st.pyplot(fig)
+
+    st.markdown("### 📍 Scatterplots: Detect Patterns or Anomalies")
+    st.markdown("These help you check combinations of features with color-coded fault info.")
+
+    fig, ax = plt.subplots()
+    sns.scatterplot(data=df, x="Brake_Pressure", y="Pad_Wear_Level", hue="Fault", palette="Set1", ax=ax)
+    ax.set_title("Brake Pressure vs Pad Wear Level")
+    st.pyplot(fig)
+
+    fig, ax = plt.subplots()
+    sns.scatterplot(data=df, x="Pedal_Position", y="Fluid_Temperature", hue="Fault", palette="Set2", ax=ax)
+    ax.set_title("Pedal Position vs Fluid Temperature")
+    st.pyplot(fig)
+
+# ----------------------------- TAB 3 ---------------------------------
+with tab3:
+    st.markdown(
+        """
+        <style>
+        .stApp {
+            background-color: #e3f2fd;
+            padding: 12px;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+    st.markdown("## 🚗 Vehicle Brake System Fault Detection")
+    st.markdown("#### Enter Brake Sensor Values to Predict Any System Fault")
+
+    # Prepare data
+    X = df.drop("Fault", axis=1)
+    y = df["Fault"]
+
+    # UI for user input
+    Brake_Pressure = st.slider("💨 Brake Pressure (psi)", 50.0, 500.0, step=0.1)
+    Pad_Wear_Level = st.slider("🛞 Pad Wear Level (%)", 0.0, 100.0, step=0.1)
+    ABS_Status = st.slider("🛑 ABS Status (0 = Off, 1 = On)", 0, 1, step=1)
+    Wheel_Speed_FL = st.slider("⚙️ Wheel Speed FL (km/h)", 0.0, 400.0, step=0.1)
+    Wheel_Speed_FR = st.slider("⚙️ Wheel Speed FR (km/h)", 0.0, 400.0, step=0.1)
+    Wheel_Speed_RL = st.slider("⚙️ Wheel Speed RL (km/h)", 0.0, 300.0, step=0.1)
+    Wheel_Speed_RR = st.slider("⚙️ Wheel Speed RR (km/h)", 0.0, 300.0, step=0.1)
+    Fluid_Temperature = st.slider("🌡️ Fluid Temperature (°C)", -20.0, 150.0, step=0.1)
+    Pedal_Position = st.slider("🦶 Pedal Position (%)", 0.0, 100.0, step=0.1)
+
+    # Train model
+    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=29)
+    model = LogisticRegression()
+    model.fit(x_train, y_train)
+
+    user_input = pd.DataFrame([[Brake_Pressure, Pad_Wear_Level, ABS_Status, Wheel_Speed_FL,
+                                Wheel_Speed_FR, Wheel_Speed_RL, Wheel_Speed_RR,
+                                Fluid_Temperature, Pedal_Position]],
+                              columns=X.columns)
+
+    if st.button("🔍 Predict Brake Fault"):
+        y_pred = model.predict(user_input)
+        prob = model.predict_proba(user_input)[0][1]
+
+        if y_pred[0] == 1:
+            st.error(f"🚨 Fault Detected in Brake System! (Confidence: {prob:.2%})")
+            issues = []
+
+            if Brake_Pressure < 60 or Brake_Pressure > 130:
+                issues.append("🔴 Abnormal Brake Pressure")
+
+            if Pad_Wear_Level >= 80:
+                issues.append("🟠 Brake Pads Critically Worn")
+            elif Pad_Wear_Level >= 60:
+                issues.append("🟡 Brake Pads Heavily Worn")
+
+            if ABS_Status == 0:
+                issues.append("🔵 ABS System Not Active")
+
+            if Wheel_Speed_FL < 0 or Wheel_Speed_FL > 130:
+                issues.append("🔴 Front Left Wheel Speed Abnormal")
+            if Wheel_Speed_FR < 0 or Wheel_Speed_FR > 130:
+                issues.append("🔴 Front Right Wheel Speed Abnormal")
+            if Wheel_Speed_RL < 0 or Wheel_Speed_RL > 130:
+                issues.append("🔴 Rear Left Wheel Speed Abnormal")
+            if Wheel_Speed_RR < 0 or Wheel_Speed_RR > 130:
+                issues.append("🔴 Rear Right Wheel Speed Abnormal")
+
+            if Fluid_Temperature < -20 or Fluid_Temperature > 120:
+                issues.append("🔥 Abnormal Brake Fluid Temperature")
+
+            if 20 < Pedal_Position < 60:
+                issues.append("🟡 Moderate Brake Pedal Pressed")
+            if 60 <= Pedal_Position <= 100:
+                issues.append("🛑 Brake Pedal Fully Pressed")
+            if Pedal_Position <= 20:
+                issues.append("🔍 Low Brake Pedal Engagement")
+
+            for issue in issues:
+                st.markdown(f"- {issue}")
+
+        else:
+            st.success(f"✅ No Fault Detected. (Confidence: {1 - prob:.2%})")
+            st.info("🚗 Your vehicle's brake system appears healthy.")

requirements (1).txt

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+streamlit
+pandas
+numpy
+scikit-learn