Update pages/Simple EDA

pages/Simple EDA

@@ -0,0 +1,46 @@
+import streamlit as st
+import pandas as pd
+
+st.set_page_config(page_title="EDA Analysis", layout="wide")
+
+st.title("Exploratory Data Analysis (EDA)")
+
+# Load stored dataset
+if "uploaded_df" in st.session_state:
+    df = st.session_state["uploaded_df"]
+    
+    # Show dataset overview
+    st.subheader("Dataset Overview")
+    st.write(f"Number of Rows: {df.shape[0]}, Number of Columns: {df.shape[1]}")
+    
+    st.subheader("First 5 Rows of Data")
+    st.write(df.head())
+
+    # Identify numerical & categorical columns
+    categorical_cols = df.select_dtypes(include=["object"]).columns.tolist()
+    numerical_cols = df.select_dtypes(exclude=["object"]).columns.tolist()
+
+    st.subheader("Column Types")
+    st.write(f"Numerical Columns: {numerical_cols}")
+    st.write(f"Categorical Columns: {categorical_cols}")
+
+    # Data Type Conversion
+    st.subheader("Convert Data Types")
+    col_to_convert = st.selectbox("Select Column to Convert", df.columns)
+    
+    conversion_options = ["Convert to Categorical", "Convert to Numerical"]
+    conversion_choice = st.radio("Choose Conversion", conversion_options)
+
+    if st.button("Apply Conversion"):
+        if conversion_choice == "Convert to Categorical":
+            df[col_to_convert] = df[col_to_convert].astype("category")
+        else:
+            df[col_to_convert] = pd.to_numeric(df[col_to_convert], errors="coerce")
+        
+        st.success(f"Column '{col_to_convert}' converted successfully!")
+
+    st.subheader("Final Data Types")
+    st.write(df.dtypes)
+
+else:
+    st.error("No dataset found. Please upload data from the main page.")