diff --git a/README.md b/README.md
index 867cc3c..c020167 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,38 @@ We implemented analytics endpoints in the backend to log user interactions with
In this [link](https://lookerstudio.google.com/reporting/8211ae21-6964-49d6-a30e-3ee5b7d009b1) you can access the live reports generated by the Analytics Pipeline. They're categorized by the following topics (following the business questions semantics):
-- Crashlytics
-- User Experience
-- Feature Usage
-- Monetization
+### Crashlytics
+
+#### Overview
+
+
+
+#### Issues Detailed
+
+
+
+### User Experience
+
+#### Meetings
+
+
+
+#### User Satisfaction
+
+
+
+#### Place Recommendations
+
+
+
+### Feature Usage
+
+#### Google Analytics
+
+
+
+### Monetization
+
+#### User Engagement (Ads)
+
+
diff --git a/dashboards/BQ2.3.pdf b/dashboards/BQ2.3.pdf
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diff --git a/excel_mockups/BQ2.3.xlsx b/excel_mockups/BQ2.3.xlsx
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diff --git a/excel_mockups/bq4.1.xlsx b/excel_mockups/bq4.1.xlsx
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diff --git a/excel_mockups/bq5.2.xlsx b/excel_mockups/bq5.2.xlsx
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diff --git a/generators/BQ2.3.py b/generators/BQ2.3.py
deleted file mode 100644
index 1d30626..0000000
--- a/generators/BQ2.3.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pandas as pd
-import numpy as np
-from faker import Faker
-fake = Faker()
-
-# Crear datos falsos para la tabla
-np.random.seed(0) # Para reproducibilidad
-num_rows = 100
-user_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-session_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-dates = [fake.date_this_year() for _ in range(num_rows)]
-start_times = [fake.time() for _ in range(num_rows)]
-end_times = [fake.time() for _ in range(num_rows)]
-sections = np.random.choice(['Calendario', 'Tareas', 'Notificaciones', 'Configuración'], num_rows)
-events = np.random.choice(['add_friend', 'chat', 'share_schedule'], num_rows)
-
-
-# Calcular duraciones de las sesiones y el tiempo en cada sección
-durations = np.random.randint(5, 120, num_rows) # Duración de sesión entre 5 y 120 minutos
-retention = np.random.randint(0, 100, num_rows) # retencion de usuario en porcentaje
-durations = np.random.randint(5, 10, num_rows) # veces que entraron a la aplicacion
-time_in_section = np.random.randint(1, durations) # Tiempo en la sección no puede ser mayor que la duración de la sesión
-interactions = np.random.randint(1, 20, num_rows) # Número de interacciones por sesión
-
-# Crear el DataFrame
-df = pd.DataFrame({
- 'UserID': user_ids,
- 'SessionID': session_ids,
- 'Fecha': dates,
- 'HoraInicio': start_times,
- 'HoraFin': end_times,
- 'DuracionSesion (minutos)': durations,
- 'Seccion': sections,
- 'TiempoEnSeccion (minutos)': time_in_section,
- 'Interacciones': interactions,
- 'Evento': events,
- 'retencion':retention,
-
-})
-
-# Define the file path where you want to save the Excel file
-file_path = 'BQ2.3.xlsx'
-
-df.to_excel(file_path, index=False)
\ No newline at end of file
diff --git a/generators/bq2.1.py b/generators/bq2.1.py
deleted file mode 100644
index 5b2f5f7..0000000
--- a/generators/bq2.1.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import pandas as pd
-import numpy as np
-
-np.random.seed(42)
-num_rows = 100
-user_ids = np.arange(1, num_rows + 1)
-user_types = np.random.choice(['Nuevo', 'Frecuente', 'Ocasional'], size=num_rows)
-
-booking_attempt_dates = pd.date_range(start="2024-01-01", end="2024-03-31", freq='8H')[:num_rows]
-
-
-buildings = ['ML', 'SD', 'O', 'W', 'RGD']
-rooms = ['102', '202', '301', '302']
-space_ids = [np.random.choice(buildings) + np.random.choice(rooms) for _ in range(num_rows)]
-
-ease_of_use_scores = np.random.randint(1, 6, size=num_rows)
-availability_scores = np.random.randint(1, 6, size=num_rows)
-overall_satisfaction_scores = np.random.randint(1, 6, size=num_rows)
-feedback_comments = np.random.choice(['Todo bien', 'Necesita mejoras', 'Excelente', 'Frustrante', 'Confuso'], size=num_rows)
-
-df_updated = pd.DataFrame({
- 'UserID': user_ids,
- 'UserType': user_types,
- 'BookingAttemptDate': booking_attempt_dates,
- 'SpaceID': space_ids,
- 'EaseOfUseScore': ease_of_use_scores,
- 'AvailabilityScore': availability_scores,
- 'OverallSatisfactionScore': overall_satisfaction_scores,
- 'FeedbackComments': feedback_comments
-})
\ No newline at end of file
diff --git a/generators/bq3.4.py b/generators/bq3.4.py
deleted file mode 100644
index 628a2e2..0000000
--- a/generators/bq3.4.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import pandas as pd
-import numpy as np
-from faker import Faker
-fake = Faker()
-
-# Crear datos falsos para la tabla
-np.random.seed(0) # Para reproducibilidad
-num_rows = 100
-user_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-session_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-dates = [fake.date_this_year() for _ in range(num_rows)]
-start_times = [fake.time() for _ in range(num_rows)]
-end_times = [fake.time() for _ in range(num_rows)]
-sections = np.random.choice(['Calendario', 'Tareas', 'Notificaciones', 'Configuración'], num_rows)
-events = np.random.choice(['Ver', 'Editar', 'Crear'], num_rows)
-costumization = np.random.choice(['BackGround_image', 'ChangeColor_Box', 'user_Icon'], num_rows)
-
-# Calcular duraciones de las sesiones y el tiempo en cada sección
-durations = np.random.randint(5, 120, num_rows) # Duración de sesión entre 5 y 120 minutos
-time_in_section = np.random.randint(1, durations) # Tiempo en la sección no puede ser mayor que la duración de la sesión
-interactions = np.random.randint(1, 20, num_rows) # Número de interacciones por sesión
-
-# Crear el DataFrame
-df = pd.DataFrame({
- 'UserID': user_ids,
- 'SessionID': session_ids,
- 'Fecha': dates,
- 'HoraInicio': start_times,
- 'HoraFin': end_times,
- 'DuracionSesion (minutos)': durations,
- 'Seccion': sections,
- 'TiempoEnSeccion (minutos)': time_in_section,
- 'Interacciones': interactions,
- 'Evento': events
-})
-
-df.to_excel("bq3.4", index=False)
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diff --git a/generators/bq4.1.py b/generators/bq4.1.py
deleted file mode 100644
index 17ed42b..0000000
--- a/generators/bq4.1.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import pandas as pd
-import numpy as np
-from faker import Faker
-fake = Faker()
-
-# Crear datos falsos para la tabla
-np.random.seed(0) # Para reproducibilidad
-num_rows = 100
-user_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-session_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-dates = [fake.date_this_year() for _ in range(num_rows)]
-start_times = [fake.time() for _ in range(num_rows)]
-end_times = [fake.time() for _ in range(num_rows)]
-sections = np.random.choice(['Calendario', 'Tareas', 'Notificaciones', 'Configuración'], num_rows)
-events = np.random.choice(['Ver', 'Editar', 'Crear'], num_rows)
-
-# Calcular duraciones de las sesiones y el tiempo en cada sección
-durations = np.random.randint(5, 120, num_rows) # Duración de sesión entre 5 y 120 minutos
-time_in_section = np.random.randint(1, durations) # Tiempo en la sección no puede ser mayor que la duración de la sesión
-interactions = np.random.randint(1, 20, num_rows) # Número de interacciones por sesión
-
-# Crear el DataFrame
-df = pd.DataFrame({
- 'UserID': user_ids,
- 'SessionID': session_ids,
- 'Fecha': dates,
- 'HoraInicio': start_times,
- 'HoraFin': end_times,
- 'DuracionSesion (minutos)': durations,
- 'Seccion': sections,
- 'TiempoEnSeccion (minutos)': time_in_section,
- 'Interacciones': interactions,
- 'Evento': events
-})
-
-df.to_excel(file_path, index=False)
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diff --git a/generators/bq5.2.py b/generators/bq5.2.py
deleted file mode 100644
index c763313..0000000
--- a/generators/bq5.2.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import pandas as pd
-import numpy as np
-from faker import Faker
-import random
-
-np.random.seed(0)
-
-fake = Faker()
-
-# Generate random data for the dataset
-num_rows = 1000
-
-# Generate UserIDs
-user_ids = [fake.unique.uuid4() for _ in range(num_rows)]
-
-# Generate UserType: Nuevo, Ocasional, Frecuente
-user_types = ['Nuevo', 'Ocasional', 'Frecuente']
-user_type = [random.choice(user_types) for _ in range(num_rows)]
-
-# Generate UserSemester: Values between 1 and 10
-user_semester = [random.randint(1, 10) for _ in range(num_rows)]
-
-# Generate UserCareer: ISIS, IIND, MATE, IBIO, IELE, IMEC, IQUI, ICYA, LITE, PSIC, MEDI
-user_careers = ["ISIS", "IIND", "MATE", "IBIO", "IELE", "IMEC", "IQUI", "ICYA", "LITE", "PSIC", "MEDI"]
-user_career = [random.choice(user_careers) for _ in range(num_rows)]
-
-# Generate MeetingDate
-meeting_dates = [fake.date_this_year() for _ in range(num_rows)]
-
-# Generate DayOfWeek
-days_of_week = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
-day_of_week = [random.choice(days_of_week) for _ in range(num_rows)]
-
-# Generate MeetingStartTime: Values between 8:00 and 17:00
-meeting_start_times = [f"{random.randint(8, 16)}:{random.choice(['00', '30'])}" for _ in range(num_rows)]
-
-# Generate MeetingDuration: Up to 4 hours
-meeting_duration = [random.randint(15, 240) for _ in range(num_rows)]
-
-# Generate MeetingBuilding: ML, SD, W, R, O, C, LL, B, RGD, AU
-meeting_buildings = ["ML", "SD", "W", "R", "O", "C", "LL", "B", "RGD", "AU"]
-meeting_building = [random.choice(meeting_buildings) for _ in range(num_rows)]
-
-# Generate MeetingPurpose: Class, Leisure, Group Project, Other
-meeting_purposes = ["Class", "Leisure", "Group Project", "Other"]
-meeting_purpose = [random.choice(meeting_purposes) for _ in range(num_rows)]
-
-# Generate OverallSatisfactionScore: Values between 1 and 5
-overall_satisfaction_score = [random.randint(1, 5) for _ in range(num_rows)]
-
-# Create the DataFrame
-df = pd.DataFrame({
- 'UserID': user_ids,
- 'UserType': user_type,
- 'UserSemester': user_semester,
- 'UserCareer': user_career,
- 'MeetingDate': meeting_dates,
- 'DayOfWeek': day_of_week,
- 'MeetingStartTime': meeting_start_times,
- 'MeetingDuration': meeting_duration,
- 'MeetingBuilding': meeting_building,
- 'MeetingPurpose': meeting_purpose,
- 'OverallSatisfactionScore': overall_satisfaction_score
-})
-
-# Display the DataFrame
-print(df.head())
-
-file_path = "./meeting_data.xlsx"
-df.to_excel(file_path, index=False)