fn-registry agent
896 lines
31 KiB
Plaintext
896 lines
31 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a0b1c2d3",
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"metadata": {},
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"source": [
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"# 03 - EDA Combinado: Happy Robot + Call Transactions\n",
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"\n",
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"Analisis cruzado entre las llamadas procesadas por el agente IA (Happy Robot) y las transacciones del call center.\n",
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"\n",
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"**Tablas:**\n",
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"- `happy_robot_publicpublic.run_events` (72 filas) - eventos del agente IA para operaciones de lunas\n",
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"- `psql_dcpublic.call_transactions` (24.7M filas) - transacciones del call center\n",
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"\n",
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"**Join bridges:**\n",
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"1. Telefono: `source` <-> `telefonos` / `orig` (normalizando +34, espacios, asteriscos)\n",
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"2. Matricula: `license_plate` <-> `cliente`\n",
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"3. Ventana temporal: `time` <-> `date_time` (proximidad en minutos)\n",
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"4. Filtro de campana: campanas Mutua en call_transactions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b1c2d3e4",
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"metadata": {},
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"source": [
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"## 1. Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c2d3e4f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as go\n",
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"from dotenv import load_dotenv\n",
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"load_dotenv()\n",
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"\n",
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"import sys\n",
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"sys.path.insert(0, os.path.join(os.environ[\"FN_REGISTRY_ROOT\"], \"python\", \"functions\"))\n",
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"from metabase.client import MetabaseClient\n",
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"\n",
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"client = MetabaseClient(os.environ[\"METABASE_URL\"], os.environ[\"METABASE_API_KEY\"])\n",
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"\n",
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"def query_to_df(sql: str) -> pd.DataFrame:\n",
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" \"\"\"Execute SQL against BigQuery via Metabase API, return DataFrame.\"\"\"\n",
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" result = client.request('POST', '/api/dataset', json={\n",
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" 'database': 6, 'type': 'native', 'native': {'query': sql}\n",
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" })\n",
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" cols = [c['name'] for c in result['data']['cols']]\n",
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" return pd.DataFrame(result['data']['rows'], columns=cols)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3e4f5a6",
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"metadata": {},
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"source": [
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"## 2. Carga de run_events"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e4f5a6b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_re = query_to_df(\"\"\"\n",
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"SELECT *\n",
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"FROM `happy_robot_publicpublic.run_events`\n",
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"ORDER BY time\n",
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"\"\"\")\n",
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"\n",
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"print(f\"run_events: {len(df_re)} filas, {len(df_re.columns)} columnas\")\n",
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"df_re.dtypes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f5a6b7c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Parsear datetimes y columnas numericas\n",
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"df_re['time'] = pd.to_datetime(df_re['time'])\n",
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"if 'duration' in df_re.columns:\n",
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" df_re['duration'] = pd.to_numeric(df_re['duration'], errors='coerce')\n",
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"\n",
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"print(f\"Rango temporal: {df_re['time'].min()} -> {df_re['time'].max()}\")\n",
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"print(f\"Clasificaciones: {df_re['classification'].value_counts().to_dict()}\")\n",
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"df_re.head(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a6b7c8d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Resumen estadistico de run_events\n",
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"print(\"Columnas disponibles:\", list(df_re.columns))\n",
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"print(f\"\\nSources unicos (telefonos): {df_re['source'].nunique()}\")\n",
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"print(f\"Matriculas unicas: {df_re['license_plate'].nunique()}\")\n",
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"print(f\"Nulos en source: {df_re['source'].isna().sum()}\")\n",
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"print(f\"Nulos en license_plate: {df_re['license_plate'].isna().sum()}\")\n",
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"df_re.describe(include='all')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7c8d9e0",
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"metadata": {},
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"source": [
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"## 3. Identificar campanas Mutua en call_transactions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c8d9e0f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_mutua_campaigns = query_to_df(\"\"\"\n",
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"SELECT\n",
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" campaign_name,\n",
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" COUNT(*) as total_calls,\n",
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" MIN(date_time) as first_call,\n",
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" MAX(date_time) as last_call\n",
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"FROM `psql_dcpublic.call_transactions`\n",
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"WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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"GROUP BY campaign_name\n",
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"ORDER BY total_calls DESC\n",
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"\"\"\")\n",
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"\n",
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"print(\"Campanas Mutua encontradas:\")\n",
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"df_mutua_campaigns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d9e0f1a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Volumen reciente de campanas Mutua (desde abril 2026, periodo Happy Robot)\n",
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"df_mutua_recent = query_to_df(\"\"\"\n",
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"SELECT\n",
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" campaign_name,\n",
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" DATE(date_time) as dia,\n",
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" COUNT(*) as calls\n",
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"FROM `psql_dcpublic.call_transactions`\n",
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"WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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" AND date_time >= '2026-04-01'\n",
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"GROUP BY campaign_name, DATE(date_time)\n",
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"ORDER BY dia\n",
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"\"\"\")\n",
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"\n",
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"df_mutua_recent['calls'] = pd.to_numeric(df_mutua_recent['calls'])\n",
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"\n",
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"fig = px.bar(df_mutua_recent, x='dia', y='calls', color='campaign_name',\n",
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" title='Llamadas Mutua por dia (desde abril 2026)',\n",
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" labels={'calls': 'Llamadas', 'dia': 'Fecha', 'campaign_name': 'Campana'})\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0f1a2b3",
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"metadata": {},
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"source": [
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"## 4. Join por telefono\n",
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"\n",
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"Normalizacion:\n",
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"- `run_events.source`: formato `+34623122972` -> extraer `623122972`\n",
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"- `call_transactions.telefonos`: formato ` 623122972` (espacio delante) -> strip\n",
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"- `call_transactions.orig`: formato ` *34623122972` -> strip `*34`\n",
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"\n",
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"Join: telefono normalizado + mismo dia"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f1a2b3c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_phone_join = query_to_df(\"\"\"\n",
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"WITH re AS (\n",
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" SELECT *,\n",
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" REPLACE(REPLACE(source, '+34', ''), ' ', '') AS phone_clean\n",
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" FROM `happy_robot_publicpublic.run_events`\n",
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"),\n",
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"ct AS (\n",
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" SELECT *,\n",
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" REPLACE(REPLACE(REPLACE(TRIM(telefonos), '*34', ''), '+34', ''), ' ', '') AS phone_clean\n",
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" FROM `psql_dcpublic.call_transactions`\n",
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" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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" AND date_time >= '2026-04-01'\n",
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")\n",
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"SELECT\n",
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" re.id AS re_id,\n",
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" re.run_id,\n",
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" re.time AS re_time,\n",
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" re.classification,\n",
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" ct.id AS ct_id,\n",
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" ct.date_time AS ct_time,\n",
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" ct.campaign_name,\n",
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" ct.description AS ct_resolution,\n",
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" ct.agente,\n",
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" ct.t_convers\n",
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"FROM re\n",
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"JOIN ct ON re.phone_clean = ct.phone_clean\n",
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" AND DATE(re.time) = DATE(ct.date_time)\n",
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"ORDER BY re.time\n",
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"\"\"\")\n",
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"\n",
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"print(f\"Matches por telefono + mismo dia: {len(df_phone_join)}\")\n",
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"print(f\"run_events distintos con match: {df_phone_join['re_id'].nunique()}\")\n",
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"df_phone_join.head(20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2b3c4d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Tambien probar join via campo orig (formato *34...)\n",
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"df_phone_join_orig = query_to_df(\"\"\"\n",
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"WITH re AS (\n",
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" SELECT *,\n",
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" REPLACE(REPLACE(source, '+34', ''), ' ', '') AS phone_clean\n",
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" FROM `happy_robot_publicpublic.run_events`\n",
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"),\n",
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"ct AS (\n",
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" SELECT *,\n",
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" REPLACE(REPLACE(REPLACE(TRIM(orig), '*34', ''), '+34', ''), ' ', '') AS phone_clean\n",
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" FROM `psql_dcpublic.call_transactions`\n",
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" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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" AND date_time >= '2026-04-01'\n",
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")\n",
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"SELECT\n",
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" re.id AS re_id,\n",
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" re.run_id,\n",
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" re.time AS re_time,\n",
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" re.classification,\n",
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" ct.id AS ct_id,\n",
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" ct.date_time AS ct_time,\n",
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" ct.campaign_name,\n",
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" ct.description AS ct_resolution,\n",
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" ct.agente,\n",
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" ct.t_convers\n",
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"FROM re\n",
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"JOIN ct ON re.phone_clean = ct.phone_clean\n",
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" AND DATE(re.time) = DATE(ct.date_time)\n",
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"ORDER BY re.time\n",
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"\"\"\")\n",
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"\n",
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"print(f\"Matches por orig + mismo dia: {len(df_phone_join_orig)}\")\n",
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"print(f\"run_events distintos con match (orig): {df_phone_join_orig['re_id'].nunique()}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b3c4d5e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Combinar ambos joins de telefono (telefonos + orig), dedup por par (re_id, ct_id)\n",
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"df_phone_all = pd.concat([df_phone_join, df_phone_join_orig]).drop_duplicates(subset=['re_id', 'ct_id'])\n",
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"print(f\"Matches telefono combinados (dedup): {len(df_phone_all)}\")\n",
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"print(f\"run_events distintos con match telefonico: {df_phone_all['re_id'].nunique()} de {len(df_re)} totales\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4d5e6f7",
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"metadata": {},
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"source": [
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"## 5. Join por matricula\n",
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"\n",
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"- `run_events.license_plate` <-> `call_transactions.cliente`\n",
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"- Ambos son matriculas (ej: `0000MHG`, `9928MKH`)\n",
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"- Join directo + mismo dia + campanas Mutua"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d5e6f7a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_plate_join = query_to_df(\"\"\"\n",
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"WITH re AS (\n",
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" SELECT *\n",
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" FROM `happy_robot_publicpublic.run_events`\n",
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" WHERE license_plate IS NOT NULL AND TRIM(license_plate) != ''\n",
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"),\n",
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"ct AS (\n",
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" SELECT *\n",
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" FROM `psql_dcpublic.call_transactions`\n",
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" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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" AND date_time >= '2026-04-01'\n",
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" AND cliente IS NOT NULL AND TRIM(cliente) != ''\n",
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")\n",
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"SELECT\n",
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" re.id AS re_id,\n",
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" re.run_id,\n",
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" re.time AS re_time,\n",
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" re.license_plate,\n",
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" re.classification,\n",
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" ct.id AS ct_id,\n",
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" ct.date_time AS ct_time,\n",
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" ct.campaign_name,\n",
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" ct.cliente,\n",
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" ct.description AS ct_resolution,\n",
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" ct.agente,\n",
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" ct.t_convers\n",
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"FROM re\n",
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"JOIN ct ON UPPER(TRIM(re.license_plate)) = UPPER(TRIM(ct.cliente))\n",
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" AND DATE(re.time) = DATE(ct.date_time)\n",
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"ORDER BY re.time\n",
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"\"\"\")\n",
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"\n",
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"print(f\"Matches por matricula + mismo dia: {len(df_plate_join)}\")\n",
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"print(f\"run_events distintos con match por matricula: {df_plate_join['re_id'].nunique()}\")\n",
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"df_plate_join.head(20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e6f7a8b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Matriculas sin match - que hay en license_plate que no aparece en cliente?\n",
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"df_no_match_plates = query_to_df(\"\"\"\n",
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"WITH re AS (\n",
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" SELECT DISTINCT UPPER(TRIM(license_plate)) AS plate\n",
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" FROM `happy_robot_publicpublic.run_events`\n",
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" WHERE license_plate IS NOT NULL AND TRIM(license_plate) != ''\n",
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"),\n",
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"ct AS (\n",
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" SELECT DISTINCT UPPER(TRIM(cliente)) AS plate\n",
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" FROM `psql_dcpublic.call_transactions`\n",
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" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
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" AND date_time >= '2026-04-01'\n",
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" AND cliente IS NOT NULL AND TRIM(cliente) != ''\n",
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")\n",
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"SELECT re.plate, \n",
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" CASE WHEN ct.plate IS NOT NULL THEN 'SI' ELSE 'NO' END AS existe_en_ct\n",
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"FROM re\n",
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"LEFT JOIN ct ON re.plate = ct.plate\n",
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"ORDER BY existe_en_ct, re.plate\n",
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"\"\"\")\n",
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"\n",
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"print(\"Matriculas de run_events y si existen en call_transactions (Mutua, abril+):\")\n",
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"df_no_match_plates"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7a8b9c0",
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"metadata": {},
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"source": [
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"## 6. Analisis de cobertura\n",
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"\n",
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"- Que % de run_events tienen match en call_transactions?\n",
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"- Que % de call_transactions Mutua recientes tienen match en run_events?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8b9c0d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cobertura combinando ambos joins (telefono + matricula)\n",
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"re_ids_phone = set(df_phone_all['re_id'].unique())\n",
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"re_ids_plate = set(df_plate_join['re_id'].unique())\n",
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"re_ids_any = re_ids_phone | re_ids_plate\n",
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"re_ids_both = re_ids_phone & re_ids_plate\n",
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"\n",
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"total_re = len(df_re)\n",
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"\n",
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"print(\"=== Cobertura de run_events ===\")\n",
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"print(f\"Total run_events: {total_re}\")\n",
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"print(f\"Match por telefono: {len(re_ids_phone)} ({100*len(re_ids_phone)/total_re:.1f}%)\")\n",
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"print(f\"Match por matricula: {len(re_ids_plate)} ({100*len(re_ids_plate)/total_re:.1f}%)\")\n",
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"print(f\"Match por cualquiera: {len(re_ids_any)} ({100*len(re_ids_any)/total_re:.1f}%)\")\n",
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"print(f\"Match por ambos: {len(re_ids_both)} ({100*len(re_ids_both)/total_re:.1f}%)\")\n",
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"print(f\"Sin match: {total_re - len(re_ids_any)} ({100*(total_re - len(re_ids_any))/total_re:.1f}%)\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b9c0d1e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cobertura inversa: que % de call_transactions Mutua recientes tienen match\n",
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"df_ct_coverage = query_to_df(\"\"\"\n",
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"WITH re_phones AS (\n",
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" SELECT DISTINCT REPLACE(REPLACE(source, '+34', ''), ' ', '') AS phone_clean\n",
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" FROM `happy_robot_publicpublic.run_events`\n",
|
|
" WHERE source IS NOT NULL\n",
|
|
"),\n",
|
|
"re_plates AS (\n",
|
|
" SELECT DISTINCT UPPER(TRIM(license_plate)) AS plate\n",
|
|
" FROM `happy_robot_publicpublic.run_events`\n",
|
|
" WHERE license_plate IS NOT NULL AND TRIM(license_plate) != ''\n",
|
|
"),\n",
|
|
"ct AS (\n",
|
|
" SELECT *,\n",
|
|
" REPLACE(REPLACE(REPLACE(TRIM(telefonos), '*34', ''), '+34', ''), ' ', '') AS phone_clean,\n",
|
|
" UPPER(TRIM(cliente)) AS plate_clean\n",
|
|
" FROM `psql_dcpublic.call_transactions`\n",
|
|
" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
|
|
" AND date_time >= '2026-04-01'\n",
|
|
")\n",
|
|
"SELECT\n",
|
|
" COUNT(*) AS total_ct_mutua,\n",
|
|
" COUNTIF(rp.phone_clean IS NOT NULL OR rpl.plate IS NOT NULL) AS matched,\n",
|
|
" COUNTIF(rp.phone_clean IS NULL AND rpl.plate IS NULL) AS unmatched\n",
|
|
"FROM ct\n",
|
|
"LEFT JOIN re_phones rp ON ct.phone_clean = rp.phone_clean\n",
|
|
"LEFT JOIN re_plates rpl ON ct.plate_clean = rpl.plate\n",
|
|
"\"\"\")\n",
|
|
"\n",
|
|
"print(\"=== Cobertura de call_transactions Mutua (abril+) ===\")\n",
|
|
"df_ct_coverage"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c0d1e2f3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Visualizacion de cobertura\n",
|
|
"coverage_data = pd.DataFrame({\n",
|
|
" 'Metodo': ['Solo telefono', 'Solo matricula', 'Ambos', 'Sin match'],\n",
|
|
" 'Count': [\n",
|
|
" len(re_ids_phone - re_ids_plate),\n",
|
|
" len(re_ids_plate - re_ids_phone),\n",
|
|
" len(re_ids_both),\n",
|
|
" total_re - len(re_ids_any)\n",
|
|
" ]\n",
|
|
"})\n",
|
|
"\n",
|
|
"fig = px.pie(coverage_data, values='Count', names='Metodo',\n",
|
|
" title=f'Cobertura de run_events ({total_re} total)',\n",
|
|
" color_discrete_sequence=px.colors.qualitative.Set2)\n",
|
|
"fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d1e2f3a4",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 7. Comparacion de resultados\n",
|
|
"\n",
|
|
"Para los pares matcheados: comparar la clasificacion de Happy Robot vs la resolucion del call center.\n",
|
|
"Las llamadas gestionadas por IA se resuelven de forma diferente?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e2f3a4b5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Join mas completo con ambos bridges para analisis de resultados\n",
|
|
"df_matched = query_to_df(\"\"\"\n",
|
|
"WITH re AS (\n",
|
|
" SELECT *,\n",
|
|
" REPLACE(REPLACE(source, '+34', ''), ' ', '') AS phone_clean\n",
|
|
" FROM `happy_robot_publicpublic.run_events`\n",
|
|
"),\n",
|
|
"ct AS (\n",
|
|
" SELECT *,\n",
|
|
" REPLACE(REPLACE(REPLACE(TRIM(telefonos), '*34', ''), '+34', ''), ' ', '') AS phone_clean,\n",
|
|
" UPPER(TRIM(cliente)) AS plate_clean\n",
|
|
" FROM `psql_dcpublic.call_transactions`\n",
|
|
" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
|
|
" AND date_time >= '2026-04-01'\n",
|
|
")\n",
|
|
"SELECT DISTINCT\n",
|
|
" re.id AS re_id,\n",
|
|
" re.run_id,\n",
|
|
" re.time AS re_time,\n",
|
|
" re.classification AS hr_classification,\n",
|
|
" re.source AS hr_phone,\n",
|
|
" re.license_plate AS hr_plate,\n",
|
|
" ct.id AS ct_id,\n",
|
|
" ct.date_time AS ct_time,\n",
|
|
" ct.campaign_name,\n",
|
|
" ct.description AS ct_resolution,\n",
|
|
" ct.agente AS ct_agente,\n",
|
|
" ct.t_convers AS ct_duration,\n",
|
|
" CASE\n",
|
|
" WHEN re.phone_clean = ct.phone_clean THEN 'telefono'\n",
|
|
" WHEN UPPER(TRIM(re.license_plate)) = ct.plate_clean THEN 'matricula'\n",
|
|
" END AS join_method\n",
|
|
"FROM re\n",
|
|
"JOIN ct ON (\n",
|
|
" (re.phone_clean = ct.phone_clean)\n",
|
|
" OR (UPPER(TRIM(re.license_plate)) = ct.plate_clean AND re.license_plate IS NOT NULL AND TRIM(re.license_plate) != '')\n",
|
|
" )\n",
|
|
" AND DATE(re.time) = DATE(ct.date_time)\n",
|
|
"ORDER BY re.time\n",
|
|
"\"\"\")\n",
|
|
"\n",
|
|
"print(f\"Pares matcheados (dedup): {len(df_matched)}\")\n",
|
|
"print(f\"\\nClasificaciones Happy Robot en matches:\")\n",
|
|
"print(df_matched['hr_classification'].value_counts())\n",
|
|
"print(f\"\\nResoluciones call center en matches:\")\n",
|
|
"print(df_matched['ct_resolution'].value_counts())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f3a4b5c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Matriz de confusion: clasificacion HR vs resolucion CT\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" confusion = pd.crosstab(\n",
|
|
" df_matched['hr_classification'],\n",
|
|
" df_matched['ct_resolution'],\n",
|
|
" margins=True\n",
|
|
" )\n",
|
|
" print(\"Matriz: HR classification (filas) vs CT resolution (columnas)\")\n",
|
|
" display(confusion)\n",
|
|
"else:\n",
|
|
" print(\"No hay pares matcheados para comparar\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a4b5c6d7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Heatmap de la comparacion\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" cross = pd.crosstab(df_matched['hr_classification'], df_matched['ct_resolution'])\n",
|
|
" fig = px.imshow(cross,\n",
|
|
" labels=dict(x='Resolucion Call Center', y='Clasificacion Happy Robot', color='Count'),\n",
|
|
" title='Happy Robot Classification vs Call Center Resolution',\n",
|
|
" text_auto=True,\n",
|
|
" color_continuous_scale='Blues')\n",
|
|
" fig.update_layout(width=900, height=500)\n",
|
|
" fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b5c6d7e8",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 8. Timeline de una llamada\n",
|
|
"\n",
|
|
"Para un par matcheado, reconstruir el ciclo de vida completo:\n",
|
|
"cuando entro en call_transactions, cuando la proceso Happy Robot, cual fue el resultado en cada sistema."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c6d7e8f9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Seleccionar un par representativo (el primero con datos completos)\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" sample = df_matched.iloc[0]\n",
|
|
" print(f\"=== Timeline de llamada ===\")\n",
|
|
" print(f\"run_event ID: {sample['re_id']}\")\n",
|
|
" print(f\"call_transaction ID: {sample['ct_id']}\")\n",
|
|
" print(f\"Metodo de join: {sample['join_method']}\")\n",
|
|
" print(f\"\")\n",
|
|
" print(f\"--- Happy Robot ---\")\n",
|
|
" print(f\" Hora: {sample['re_time']}\")\n",
|
|
" print(f\" Telefono: {sample['hr_phone']}\")\n",
|
|
" print(f\" Matricula: {sample['hr_plate']}\")\n",
|
|
" print(f\" Clasificacion: {sample['hr_classification']}\")\n",
|
|
" print(f\" Run ID: {sample['run_id']}\")\n",
|
|
" print(f\"\")\n",
|
|
" print(f\"--- Call Center ---\")\n",
|
|
" print(f\" Hora: {sample['ct_time']}\")\n",
|
|
" print(f\" Campana: {sample['campaign_name']}\")\n",
|
|
" print(f\" Resolucion: {sample['ct_resolution']}\")\n",
|
|
" print(f\" Agente: {sample['ct_agente']}\")\n",
|
|
" print(f\" Duracion conversacion: {sample['ct_duration']}s\")\n",
|
|
"else:\n",
|
|
" print(\"No hay pares matcheados\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d7e8f9a0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Detalle completo del run_event seleccionado desde la BD\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" sample_re_id = df_matched.iloc[0]['re_id']\n",
|
|
" df_re_detail = query_to_df(f\"\"\"\n",
|
|
" SELECT *\n",
|
|
" FROM `happy_robot_publicpublic.run_events`\n",
|
|
" WHERE id = '{sample_re_id}'\n",
|
|
" \"\"\")\n",
|
|
" print(\"Detalle completo del run_event:\")\n",
|
|
" for col in df_re_detail.columns:\n",
|
|
" print(f\" {col}: {df_re_detail.iloc[0][col]}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e8f9a0b1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Timeline visual: todos los matches ordenados cronologicamente\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" df_timeline = df_matched.copy()\n",
|
|
" df_timeline['re_time'] = pd.to_datetime(df_timeline['re_time'])\n",
|
|
" df_timeline['ct_time'] = pd.to_datetime(df_timeline['ct_time'])\n",
|
|
" df_timeline['diff_minutes'] = (df_timeline['re_time'] - df_timeline['ct_time']).dt.total_seconds() / 60\n",
|
|
"\n",
|
|
" fig = px.scatter(df_timeline, x='re_time', y='diff_minutes',\n",
|
|
" color='hr_classification',\n",
|
|
" hover_data=['re_id', 'ct_id', 'hr_classification', 'ct_resolution'],\n",
|
|
" title='Diferencia temporal: Happy Robot vs Call Center (minutos)',\n",
|
|
" labels={'re_time': 'Hora Happy Robot', 'diff_minutes': 'Diferencia (min)'})\n",
|
|
" fig.add_hline(y=0, line_dash='dash', line_color='gray')\n",
|
|
" fig.show()\n",
|
|
"\n",
|
|
" print(f\"\\nDiferencia temporal media: {df_timeline['diff_minutes'].mean():.1f} min\")\n",
|
|
" print(f\"Diferencia temporal mediana: {df_timeline['diff_minutes'].median():.1f} min\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f9a0b1c2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 9. Metricas cruzadas\n",
|
|
"\n",
|
|
"Para llamadas matcheadas:\n",
|
|
"- Comparacion de duracion: `run_events.duration` vs `call_transactions.t_convers`\n",
|
|
"- Tasa de exito/resolucion en cada sistema"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a0b1c2d3a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Comparar duraciones donde ambas estan disponibles\n",
|
|
"df_durations = query_to_df(\"\"\"\n",
|
|
"WITH re AS (\n",
|
|
" SELECT *,\n",
|
|
" REPLACE(REPLACE(source, '+34', ''), ' ', '') AS phone_clean\n",
|
|
" FROM `happy_robot_publicpublic.run_events`\n",
|
|
"),\n",
|
|
"ct AS (\n",
|
|
" SELECT *,\n",
|
|
" REPLACE(REPLACE(REPLACE(TRIM(telefonos), '*34', ''), '+34', ''), ' ', '') AS phone_clean\n",
|
|
" FROM `psql_dcpublic.call_transactions`\n",
|
|
" WHERE LOWER(campaign_name) LIKE '%mutua%'\n",
|
|
" AND date_time >= '2026-04-01'\n",
|
|
")\n",
|
|
"SELECT\n",
|
|
" re.id AS re_id,\n",
|
|
" re.duration AS hr_duration,\n",
|
|
" ct.t_convers AS ct_duration,\n",
|
|
" re.classification AS hr_classification,\n",
|
|
" ct.description AS ct_resolution\n",
|
|
"FROM re\n",
|
|
"JOIN ct ON re.phone_clean = ct.phone_clean\n",
|
|
" AND DATE(re.time) = DATE(ct.date_time)\n",
|
|
"WHERE re.duration IS NOT NULL AND ct.t_convers IS NOT NULL\n",
|
|
"ORDER BY re.time\n",
|
|
"\"\"\")\n",
|
|
"\n",
|
|
"if len(df_durations) > 0:\n",
|
|
" df_durations['hr_duration'] = pd.to_numeric(df_durations['hr_duration'], errors='coerce')\n",
|
|
" df_durations['ct_duration'] = pd.to_numeric(df_durations['ct_duration'], errors='coerce')\n",
|
|
"\n",
|
|
" print(f\"Pares con duracion en ambos sistemas: {len(df_durations)}\")\n",
|
|
" print(f\"\\nDuracion Happy Robot (seg):\")\n",
|
|
" print(df_durations['hr_duration'].describe())\n",
|
|
" print(f\"\\nDuracion Call Center (seg):\")\n",
|
|
" print(df_durations['ct_duration'].describe())\n",
|
|
"else:\n",
|
|
" print(\"No hay pares con duracion en ambos sistemas\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b1c2d3e4a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Scatter: duracion HR vs duracion CT\n",
|
|
"if len(df_durations) > 0 and df_durations['hr_duration'].notna().sum() > 0:\n",
|
|
" fig = px.scatter(df_durations, x='hr_duration', y='ct_duration',\n",
|
|
" color='hr_classification',\n",
|
|
" hover_data=['re_id', 'ct_resolution'],\n",
|
|
" title='Duracion: Happy Robot vs Call Center (segundos)',\n",
|
|
" labels={'hr_duration': 'Happy Robot (seg)', 'ct_duration': 'Call Center (seg)'})\n",
|
|
" # Linea diagonal (misma duracion)\n",
|
|
" max_dur = max(df_durations['hr_duration'].max(), df_durations['ct_duration'].max())\n",
|
|
" fig.add_trace(go.Scatter(x=[0, max_dur], y=[0, max_dur],\n",
|
|
" mode='lines', line=dict(dash='dash', color='gray'),\n",
|
|
" name='Igual duracion'))\n",
|
|
" fig.show()\n",
|
|
"else:\n",
|
|
" print(\"Datos de duracion insuficientes para scatter\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c2d3e4f5a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Tasa de exito por sistema\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" print(\"=== Tasas de resolucion en pares matcheados ===\")\n",
|
|
" print(f\"\\nHappy Robot - clasificaciones:\")\n",
|
|
" hr_counts = df_matched['hr_classification'].value_counts()\n",
|
|
" for cls, count in hr_counts.items():\n",
|
|
" print(f\" {cls}: {count} ({100*count/len(df_matched):.1f}%)\")\n",
|
|
"\n",
|
|
" print(f\"\\nCall Center - resoluciones:\")\n",
|
|
" ct_counts = df_matched['ct_resolution'].value_counts()\n",
|
|
" for res, count in ct_counts.items():\n",
|
|
" print(f\" {res}: {count} ({100*count/len(df_matched):.1f}%)\")\n",
|
|
"else:\n",
|
|
" print(\"No hay pares matcheados\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d3e4f5a6a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Distribucion comparada de clasificaciones\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" fig = go.Figure()\n",
|
|
"\n",
|
|
" hr_vc = df_matched['hr_classification'].value_counts()\n",
|
|
" ct_vc = df_matched['ct_resolution'].value_counts().head(10)\n",
|
|
"\n",
|
|
" fig.add_trace(go.Bar(name='Happy Robot', x=hr_vc.index, y=hr_vc.values))\n",
|
|
"\n",
|
|
" fig.update_layout(\n",
|
|
" title='Distribucion de clasificaciones - Happy Robot (pares matcheados)',\n",
|
|
" xaxis_title='Clasificacion', yaxis_title='Count',\n",
|
|
" barmode='group'\n",
|
|
" )\n",
|
|
" fig.show()\n",
|
|
"\n",
|
|
" fig2 = go.Figure()\n",
|
|
" fig2.add_trace(go.Bar(name='Call Center', x=ct_vc.index, y=ct_vc.values,\n",
|
|
" marker_color='coral'))\n",
|
|
" fig2.update_layout(\n",
|
|
" title='Distribucion de resoluciones - Call Center (pares matcheados, top 10)',\n",
|
|
" xaxis_title='Resolucion', yaxis_title='Count'\n",
|
|
" )\n",
|
|
" fig2.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e4f5a6b7a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 10. Resumen y hallazgos"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f5a6b7c8a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Resumen final cuantitativo\n",
|
|
"print(\"=\"*60)\n",
|
|
"print(\"RESUMEN: EDA Combinado Happy Robot + Call Transactions\")\n",
|
|
"print(\"=\"*60)\n",
|
|
"print(f\"\\n1. VOLUMENES\")\n",
|
|
"print(f\" run_events (Happy Robot): {total_re} filas\")\n",
|
|
"print(f\" call_transactions Mutua (abril+): ver celda 3\")\n",
|
|
"print(f\"\")\n",
|
|
"print(f\"2. COBERTURA\")\n",
|
|
"print(f\" Match por telefono: {len(re_ids_phone)} run_events ({100*len(re_ids_phone)/total_re:.1f}%)\")\n",
|
|
"print(f\" Match por matricula: {len(re_ids_plate)} run_events ({100*len(re_ids_plate)/total_re:.1f}%)\")\n",
|
|
"print(f\" Match por cualquier metodo: {len(re_ids_any)} run_events ({100*len(re_ids_any)/total_re:.1f}%)\")\n",
|
|
"print(f\" Sin match: {total_re - len(re_ids_any)} run_events ({100*(total_re - len(re_ids_any))/total_re:.1f}%)\")\n",
|
|
"print(f\"\")\n",
|
|
"print(f\"3. JOIN BRIDGES\")\n",
|
|
"print(f\" Telefono: source(+34...) <-> telefonos/orig, normalizado, mismo dia\")\n",
|
|
"print(f\" Matricula: license_plate <-> cliente, mismo dia\")\n",
|
|
"print(f\" Filtro: solo campanas Mutua en call_transactions\")\n",
|
|
"print(f\"\")\n",
|
|
"print(f\"4. PARES MATCHEADOS: {len(df_matched)}\")\n",
|
|
"if len(df_matched) > 0:\n",
|
|
" print(f\" Clasificaciones HR: {df_matched['hr_classification'].value_counts().to_dict()}\")\n",
|
|
" print(f\" Resoluciones CT (top 5): {df_matched['ct_resolution'].value_counts().head(5).to_dict()}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a6b7c8d9a",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Hallazgos clave\n",
|
|
"\n",
|
|
"**Pendiente de rellenar tras ejecucion:**\n",
|
|
"\n",
|
|
"1. **Tasa de matching**: X% de las llamadas de Happy Robot se encuentran en call_transactions\n",
|
|
"2. **Metodo de join mas efectivo**: telefono vs matricula\n",
|
|
"3. **Consistencia de resultados**: como se comparan las clasificaciones del agente IA con las resoluciones humanas\n",
|
|
"4. **Diferencia temporal**: cuanto tiempo pasa entre el registro en cada sistema\n",
|
|
"5. **Duracion comparada**: llamadas del agente IA son mas cortas/largas que las humanas?\n",
|
|
"6. **Campanas relevantes**: que campanas Mutua especificas solapan con Happy Robot\n",
|
|
"\n",
|
|
"### Proximos pasos\n",
|
|
"\n",
|
|
"- Ampliar ventana temporal si el matching es bajo (probar +/- 1 dia en vez de mismo dia)\n",
|
|
"- Analizar las llamadas sin match: por que no aparecen en call_transactions?\n",
|
|
"- Comparar con campanas no-Mutua por si hay llamadas mal clasificadas\n",
|
|
"- Evaluar si Happy Robot reduce la carga del call center o genera llamadas duplicadas"
|
|
]
|
|
}
|
|
],
|
|
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|
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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