bizanalyticsai

pip install bizanalyticsai

# Ou com extras
pip install bizanalyticsai[reports,charts]

from bizanalyticsai import ClientAnalyzer

analyzer = ClientAnalyzer.from_csv("clientes.csv")

# Score RFM (Recência, Frequência, Valor)
rfm = analyzer.rfm_score()
print(rfm.top_clients(n=10))

# Segmentação automática por IA
segments = analyzer.segment(n_clusters=4)
segments.plot_heatmap()  # Exporta HTML

# Churn prediction
churn = analyzer.predict_churn(threshold=0.7)
churn.export_csv("churn_risk.csv")

from bizanalyticsai import FinancialReport

report = FinancialReport(
    revenue=[45000, 52000, 61000, 58000, 67000, 72000],
    costs=[32000, 36000, 41000, 39000, 45000, 48000],
    months=["Jan", "Fev", "Mar", "Abr", "Mai", "Jun"],
)

# Gera relatório HTML completo
report.generate_html("relatorio_financeiro.html")

# DRE automático
dre = report.dre()
print(f"Margem bruta: {dre.gross_margin:.1%}")
print(f"EBITDA: R$ {dre.ebitda:,.0f}")

# Ponto de equilíbrio
breakeven = report.breakeven_analysis()
breakeven.plot()

from bizanalyticsai import MarketAnalysis

market = MarketAnalysis(sector="fintech", country="BR")

# TAM / SAM / SOM
sizing = market.market_sizing(
    total_addressable=5e9,
    serviceable_pct=0.15,
    obtainable_pct=0.03,
)
print(sizing.summary())

# Tendências via API pública
trends = market.get_trends(keywords=["pix", "open banking"])
trends.to_dataframe()

# Relatório de competidores
comp = market.competitor_analysis(
    competitors=["Nubank", "Inter", "PicPay"]
)
comp.export_html("competidores.html")