Create EDA and Feature Engineering.py

pages/EDA and Feature Engineering.py

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+import streamlit as st
+import pandas as pd
+
+# EDA and Feature Engineering Page
+st.title("EDA and Feature Engineering")
+st.markdown("""
+This section is dedicated to exploratory data analysis (EDA) and feature engineering. 
+You can upload your dataset and analyze it here.
+""")
+
+# File uploader for dataset
+uploaded_file = st.file_uploader("Upload your dataset (CSV format):", type=["csv"])
+
+if uploaded_file is not None:
+    # Read and display the dataset
+    data = pd.read_csv(uploaded_file)
+    st.write("### Uploaded Dataset:")
+    st.dataframe(data)
+
+    # Overview of the dataset
+    st.write("### Dataset Overview:")
+    st.write(data.describe())
+
+    # Missing values in the dataset
+    st.write("### Missing Values:")
+    st.write(data.isnull().sum())
+
+    # Correlation matrix
+    st.write("### Correlation Matrix:")
+    st.write(data.corr())
+
+    st.markdown("""
+    Based on the insights from this analysis, you can proceed to perform feature engineering by:
+    - Handling missing values.
+    - Creating or transforming features.
+    - Encoding categorical variables.
+    - Normalizing or scaling numerical features.
+    """)
+else:
+    st.warning("Please upload a dataset to proceed with EDA.")