Delete final.py

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 sklearn
-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"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.")