Create pages/2 Data Understanding.py

pages/2 Data Understanding.py

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+import streamlit as st
+
+# 📥 Data Collection & Understanding
+st.markdown("""
+## 📥 Data Collection & Data Understanding
+
+### 📦 Data Collection:
+
+The dataset used in this project was sourced from a structured collection of industrial and residential project data.  
+It includes various **environmental**, **team-related**, and **budget-related attributes** that influence the overall risk level of a project.
+
+### 🔑 Dataset Features:
+
+- **Project Type**: Type of the project (e.g., Construction, IT, Manufacturing, etc.)
+- **Team Size**: Number of people working on the project
+- **Project Budget (USD)**: Total budget allocated to the project in U.S. dollars
+- **Estimated Timeline (Months)**: Duration estimated to complete the project
+- **Complexity Score**: A numerical value representing the technical and operational complexity of the project
+- **Stakeholder Count**: Number of stakeholders involved in decision-making
+- **Methodology Used**: Project management approach adopted (e.g., Agile, Waterfall)
+- **Team Experience Level88: Average experience level of the team (e.g., Junior, Mid, Senior)
+- **External Dependencies Count**: Number of third-party or external factors the project relies on
+- **Requirement Stability**: Indicates how frequently the project requirements change (e.g., Stable, Unstable)
+- **Current Phase Duration (Months)**: Time spent in the current project phase
+- **Risk Level (Target)**: The overall predicted risk level of the project (Low, Medium, High)  
+
+### 📚 Key Understanding:
+
+- **Data Type**: A mix of **categorical** and **numerical** features  
+- **Data Size**: Moderate-sized dataset suitable for training classification models  
+- **Target Variable**: **Risk Level** — classified as **Low**, **Medium**, or **High** based on the input attributes
+""")
+
+if st.button('Next'):
+    st.switch_page(r"pages\3.EDA.py")
+
+if st.button('Back'):
+    st.switch_page(r"pages\1.Problem Statement.py")