""" AI-Powered Marketing Automation & Lead Generation for SMEs Main entry-point / Home page """ import streamlit as st from utils.helpers import PHASE_DESCRIPTIONS, load_leads, load_bank st.set_page_config( page_title="AI Marketing Automation – SME", page_icon="πŸš€", layout="wide", initial_sidebar_state="expanded", ) # ── Custom CSS ──────────────────────────────────────────────────────────────── st.markdown(""" """, unsafe_allow_html=True) # ── Hero Banner ─────────────────────────────────────────────────────────────── st.markdown("""

πŸš€ AI-Powered Marketing Automation

Lead Generation & Scoring Framework for Small and Medium Enterprises (SMEs)
Bank Marketing Dataset (UCI)  |  Lead Scoring Dataset (Kaggle)

""", unsafe_allow_html=True) # ── Quick dataset stats ─────────────────────────────────────────────────────── with st.spinner("Loading datasets…"): leads = load_leads() bank = load_bank() c1, c2, c3, c4, c5, c6 = st.columns(6) stats = [ (f"{len(leads):,}", "Lead Records"), (f"{leads.shape[1]}", "Lead Features"), (f"{leads['Converted'].mean()*100:.1f}%", "Lead Conv. Rate"), (f"{len(bank):,}", "Bank Records"), (f"{bank.shape[1]}", "Bank Features"), (f"{(bank['y']=='yes').mean()*100:.1f}%", "Bank Sub. Rate"), ] for col, (val, lbl) in zip([c1,c2,c3,c4,c5,c6], stats): col.markdown(f'
{val}
' f'
{lbl}
', unsafe_allow_html=True) st.markdown("---") # ── Phase Overview ──────────────────────────────────────────────────────────── st.markdown("### πŸ“‹ Implementation Phases") st.markdown("Navigate through each phase using the **sidebar** on the left.") cols = st.columns(3) for idx, (phase_num, (title, desc)) in enumerate(PHASE_DESCRIPTIONS.items()): with cols[idx % 3]: st.markdown( f'
' f'Phase {phase_num}
' f'{title}
' f'{desc}' f'
', unsafe_allow_html=True, ) st.markdown("---") # ── Research Goals Summary ──────────────────────────────────────────────────── with st.expander("πŸ“– Research Goals & Background"): st.markdown(""" **This system implements the research framework from:** > *AI-Powered Marketing Automation and Lead Generation for SMEs* #### Key Research Goals | Goal | Description | |------|-------------| | **G1 – Integrated AI Framework** | Combine classification, NLP, and conversational AI | | **G2 – Lead Scoring** | Evaluate LR, RF, XGBoost, LightGBM, SVM, MLP | | **G3 – Empirical Validation** | Benchmark on Kaggle Leads (~9k) & UCI Bank (~45k) | | **G4 – NLP Effectiveness** | Intent, sentiment, engagement signal extraction | | **G5 – Conversational AI** | Chatbot for 24/7 lead qualification | | **G6 – Ethics & Privacy** | Bias detection, GDPR compliance, explainability | | **G7 – ROI Analysis** | Quantify conversion uplift and cost savings | #### Datasets - **Leads Dataset** – EdTech platform, ~9,240 records, 37 features, 38.5% conversion rate - **Bank Marketing Dataset** – Portuguese bank, ~45,211 records, 17 features, 11.7% subscription rate """) st.info("πŸ‘ˆ **Use the sidebar to navigate between phases.** Start with **Phase 1 – Data Loading**.")