Update EDA.py

EDA.py

@@ -1,44 +1,45 @@
-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)
-
-
-
+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)
+
+
+