Attendance Analytics: Lagos Climate Summit 2024

1. Executive Summary

Attendance Analytics: Lagos Climate Summit 2024

What factors predict whether a pre-registered visitor will attend — and how can this drive smarter event management?

1,765 Total Registrations

55.8% No-Show Rate

44.2% Attendance Rate

0.779 Model AUC

This analysis investigates what factors predict attendance at the Lagos Climate Summit 2024, drawing on registration data for 1,765 participants. With a 55.8% no-show rate, understanding the drivers of non-attendance is directly relevant to post-event reporting and future stakeholder engagement planning. Five analytical techniques — EDA, visualisation, hypothesis testing, correlation, and logistic regression — were applied to identify actionable predictors of attendance. The analysis finds that mode of attendance and registration timing are the dominant predictors. Virtual registrants had a 92.5% no-show rate, and attendees registered on average two days earlier than no-shows. The logistic regression model achieved an AUC of 0.779. The key recommendation is a tiered automated reminder system prioritising virtual and late registrants.

2. Professional Disclosure

Job Title: Creative Director

Organisation: City Slick Events

Sector: Events Management and Media Production

Relevance of each technique to my role:

Exploratory Data Analysis: As Creative Director at City Slick Events, I am responsible for overseeing the end-to-end delivery of large-scale events including government summits, corporate activations, and public gatherings. EDA is directly relevant to my role because every post-event report I produce for clients begins with a systematic review of attendance data — checking for completeness, identifying anomalies in registration records, and establishing baseline participation rates across different guest categories. At the scale of the Lagos Climate Summit, with 1,765 registrants across multiple categories and modes, profiling the data before drawing any conclusions is an essential quality control step that protects the integrity of my client reporting.

Data Visualisation: A core deliverable of my role is presenting event performance summaries to clients, sponsors, and government stakeholders. Visualisation is directly relevant because complex attendance patterns — such as no-show rates by category or the relationship between registration timing and actual attendance — must be communicated clearly and quickly to non-technical decision-makers. Interactive charts allow clients to explore the data themselves during debrief sessions rather than relying solely on static slide decks, which strengthens confidence in our post-event analysis and supports renewal of event management contracts.

Hypothesis Testing: My team regularly debates operational questions with direct budget implications — for example, whether virtual registration genuinely drives lower attendance than physical, or whether last-minute registrations are a reliable predictor of no-shows. Without formal hypothesis testing, these debates are resolved by anecdote or individual experience rather than statistical evidence. Applying chi-squared and Mann-Whitney tests gives me statistically rigorous answers that I can present to clients as evidence-based recommendations rather than assumptions, which is particularly important when advising on event format decisions and registration policy changes.

Correlation Analysis: Understanding which registration characteristics co-vary with attendance probability is directly relevant to how City Slick Events designs its pre-event outreach strategy. Correlation analysis allows me to identify which variables — mode of registration, lead time, category — are most strongly associated with whether a registrant will actually show up. This directly informs decisions about which guest segments should receive priority reminder communications, how far in advance reminders should be sent, and where to focus client engagement resources before large-scale events.

Logistic Regression: My role involves advising clients on how to reduce no-show rates and improve event ROI. A logistic regression model that produces an individual attendance probability score for each registrant allows City Slick Events to move from blanket reminder communications to targeted, risk-scored outreach — sending the highest-intensity follow-up to registrants most likely to not attend. This transforms our pre-event communication strategy from a volume-based approach to a data-driven one, reducing cost per confirmed attendee and improving the overall accuracy of our capacity planning.

3. Data Collection & Sampling

Source: Lagos Climate Summit 2024 official registration system, managed by the Lagos State Government organising committee

Collection method: The dataset was exported directly from Registration Link — the event registration platform used by the Lagos Climate Summit 2024 organising team — as a Microsoft Excel workbook (.xlsx) containing all pre-registration records logged between 8 May 2024 and 13 June 2024. The workbook was structured with two separate sheets: one for attendees admitted on the event day and one for no-shows. City Slick Events received access to this export as part of the post-event reporting and stakeholder engagement review process.

Tools used: Data was exported from Registration Link using the platform’s built-in export function. The exported Excel file was imported into RStudio for cleaning and analysis using the readxl package (Wickham & Bryan, 2025). Python analysis was conducted using pandas for data manipulation, scipy for statistical testing, and plotly for interactive visualisation.

Sampling frame: All individuals who completed online pre-registration for the Lagos Climate Summit held on 13 June 2024, regardless of category, mode of attendance, or final admission status.

Sample size: 1,765 registration records across 12 variables (780 attended; 985 did not attend).

Time period covered: 8 May 2024 to 13 June 2024 (37 days)

Variables collected: Booking ID, registration date, category (Visitor, Delegate, Speaker, Official, VIP), mode of attendance (Physical, Virtual), pre-registration status, country of origin, admission status (Yes/No), and computed variables: registration lead time in days, registration week, and attendance binary flag.

Variable inventory:

Variable	Type	Role
Admitted	Categorical (Yes/No)	Outcome variable
Category	Categorical	Predictor
Mode of Attendance	Categorical	Predictor
Pre_Reg	Categorical	Predictor
Country	Categorical	Predictor
Date_Reg	Date	Predictor
reg_lead_days	Numeric	Derived predictor
admitted_bin	Binary (0/1)	Numeric outcome
mode_clean	Categorical	Cleaned predictor
is_nigeria	Categorical	Derived predictor
reg_week	Date	Temporal grouping
category_analysis	Categorical	Cleaned predictor

Sampling rationale: A census approach was used — all 1,765 registration records for the event were included in the analysis rather than a random sample, as the complete population was available and accessible. No sampling bias is introduced by exclusion. The dataset exceeds the CS1 minimum of 100 observations by a factor of 17, providing ample statistical power for logistic regression with three predictors at the conventional α = 0.05 significance level. The 37-day coverage window captures the full registration lifecycle from opening to event day.

Ethical notes: All personally identifiable information — surnames, first names, email addresses, and phone numbers — has been removed before publication. Booking IDs replace real identifiers. Organisation names are retained as they are not personally identifiable. Data was shared with City Slick Events by the organising body for the purposes of post-event reporting and academic analysis, with permission obtained prior to submission.

Data sharing restrictions: The dataset has been anonymised in accordance with the Lagos Climate Summit organising committee’s data governance requirements. No personally identifiable information is published in this document. The data is used exclusively for academic purposes and will not be shared with third parties.

Dataset citation: Olajide, O. (2024). Lagos Climate Summit 2024 registration records [Dataset]. Exported from Registration Link event registration platform, Lagos State Government organising committee, Lagos, Nigeria. Data available on request from the author.

4. Data Description

library(tidyverse)
library(readxl)
library(skimr)
library(lubridate)
library(plotly)
library(heatmaply)
library(rstatix)
library(broom)
library(pROC)
library(kableExtra)
library(coin)

attended <- read_excel("data/Climate_Summit.xlsx",
                       sheet = "Attended")
noshow   <- read_excel("data/Climate_Summit.xlsx",
                       sheet = "No show")

df <- bind_rows(attended, noshow) |>
  select(-Surname, -Firstname, -Email, -Phone, -Description) |>
  mutate(
    admitted_bin      = if_else(Admitted == "Yes", 1L, 0L),
    reg_lead_days     = as.numeric(
                          as.Date("2024-06-13") -
                          as.Date(Date_Reg)),
    reg_lead_days     = if_else(reg_lead_days < 0,
                                NA_real_, reg_lead_days),
    mode_clean        = if_else(
                          `Mode of Attendance` %in%
                            c("Physical","Virtual"),
                          `Mode of Attendance`, NA_character_),
    is_nigeria        = if_else(
                          Country == "Nigeria" | is.na(Country),
                          "Nigeria", "International"),
    reg_week          = floor_date(as.Date(Date_Reg), "week"),
    category_analysis = if_else(
                          Category %in% c("Official","VIP"),
                          "Other", Category)
  )

cat("Total registrations:", nrow(df), "\n")

Total registrations: 1765 

cat("Attended:", sum(df$admitted_bin), "\n")

Attended: 780 

cat("No-show:", sum(df$admitted_bin == 0), "\n")

No-show: 985 

cat("Variables:", ncol(df), "\n")

Variables: 17 

cat("Numeric variables: reg_lead_days, admitted_bin\n")

Numeric variables: reg_lead_days, admitted_bin

cat("Categorical: Category, mode_clean, Pre_Reg,",
    "is_nigeria, category_analysis\n")

Categorical: Category, mode_clean, Pre_Reg, is_nigeria, category_analysis

cat("Date variables: Date_Reg, reg_week\n")

Date variables: Date_Reg, reg_week

5. Exploratory Data Analysis

Technique 1 — Exploratory Data Analysis (Adi, 2026, Ch. 9 — markanalytics.online)

Theory: EDA is the process of summarising, visualising, and understanding the structure of a dataset before formal modelling. It involves identifying missing values, outliers, distributional patterns, and data quality issues that could bias results if left unaddressed (Adi, 2026, Ch. 9).

Business justification: Before drawing any conclusions about attendance drivers, I must first understand the quality of the registration data, identify any inconsistencies introduced during data entry, and establish baseline attendance rates across all key dimensions.

Technique justification: EDA is the appropriate first technique because the dataset is an organisational export with unknown quality issues. Without profiling the data first, any subsequent statistical tests or models could be built on flawed foundations.

R

skim(df |> select(admitted_bin, reg_lead_days, Category,
                  Pre_Reg, mode_clean, is_nigeria))

Data summary

Name	select(…)
Number of rows	1765
Number of columns	6
_______________________
Column type frequency:
character	4
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Category	0	1.00	3	8	0	5	0
Pre_Reg	0	1.00	2	3	0	2	0
mode_clean	274	0.84	7	8	0	2	0
is_nigeria	0	1.00	7	13	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
admitted_bin	0	1	0.44	0.50	0	0	0	1	1	▇▁▁▁▆
reg_lead_days	0	1	7.15	6.64	0	2	6	8	36	▇▂▁▁▁

df |>
  count(`Mode of Attendance`) |>
  kbl(caption = "Issue 1: Mode of Attendance — Raw Values") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Issue 1: Mode of Attendance — Raw Values

Mode of Attendance	n
5	1
Physical	1197
Virtual	294
NA	273

df |>
  summarise(across(everything(), ~sum(is.na(.)))) |>
  pivot_longer(everything(),
               names_to  = "Variable",
               values_to = "Missing") |>
  filter(Missing > 0) |>
  arrange(desc(Missing)) |>
  kbl(caption = "Issue 2: Missing Values by Variable") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Issue 2: Missing Values by Variable

Variable	Missing
mode_clean	274
Mode of Attendance	273
City	122
Country	122
Designation	51
Organization	25

df |>
  count(Admitted) |>
  mutate(pct = round(n / sum(n) * 100, 1)) |>
  kbl(caption = "Overall Attendance vs No-Show",
      col.names = c("Admitted", "Count", "Percentage (%)")) |>
  kable_styling(bootstrap_options = c("striped","hover"),
                full_width = FALSE) |>
  row_spec(1, background = "#fde8e4") |>
  row_spec(2, background = "#dcfce7")

Overall Attendance vs No-Show

Admitted	Count	Percentage (%)
No	985	55.8
Yes	780	44.2

df |>
  group_by(Category) |>
  summarise(Total    = n(),
            Attended = sum(admitted_bin),
            `Rate (%)` = round(mean(admitted_bin) * 100, 1)) |>
  arrange(desc(`Rate (%)`)) |>
  kbl(caption = "Attendance Rate by Category") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Attendance Rate by Category

Category	Total	Attended	Rate (%)
Delegate	89	89	100.0
Official	2	2	100.0
Speaker	29	29	100.0
VIP	1	1	100.0
Visitor	1644	659	40.1

df |>
  filter(!is.na(mode_clean)) |>
  group_by(mode_clean) |>
  summarise(Total    = n(),
            Attended = sum(admitted_bin),
            `Rate (%)` = round(mean(admitted_bin) * 100, 1)) |>
  kbl(caption = "Attendance Rate by Mode") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Attendance Rate by Mode

mode_clean	Total	Attended	Rate (%)
Physical	1197	597	49.9
Virtual	294	22	7.5

df |>
  group_by(Pre_Reg) |>
  summarise(Total    = n(),
            Attended = sum(admitted_bin),
            `Rate (%)` = round(mean(admitted_bin) * 100, 1)) |>
  kbl(caption = "Attendance Rate by Pre-Registration Status") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Attendance Rate by Pre-Registration Status

Pre_Reg	Total	Attended	Rate (%)
No	121	121	100.0
Yes	1644	659	40.1

Python

print(f"Shape: {df.shape[0]} rows x {df.shape[1]} columns\n")

Shape: 1765 rows x 17 columns

cols = ["admitted_bin","reg_lead_days","Category",
        "Pre_Reg","mode_clean","is_nigeria"]
summary = df[cols].describe(include="all").T
summary.insert(0, "dtype", df[cols].dtypes.values)
print(summary.to_string())

                 dtype   count unique       top  freq      mean       std  min  25%  50%  75%   max
admitted_bin     int32  1765.0    NaN       NaN   NaN  0.441926  0.496757  0.0  0.0  0.0  1.0   1.0
reg_lead_days  float64  1765.0    NaN       NaN   NaN  7.151841  6.644662  0.0  2.0  6.0  8.0  36.0
Category        object    1765      5   Visitor  1644       NaN       NaN  NaN  NaN  NaN  NaN   NaN
Pre_Reg         object    1765      2       Yes  1644       NaN       NaN  NaN  NaN  NaN  NaN   NaN
mode_clean      object    1491      2  Physical  1197       NaN       NaN  NaN  NaN  NaN  NaN   NaN
is_nigeria      object    1765      2   Nigeria  1752       NaN       NaN  NaN  NaN  NaN  NaN   NaN

print("Issue 1: Mode of Attendance — Raw Value Counts")

Issue 1: Mode of Attendance — Raw Value Counts

print(df["Mode of Attendance"].value_counts(dropna=False).to_string())

Mode of Attendance
Physical    1197
Virtual      294
None         273
5              1

print("\nIssue 2: Missing Values by Variable")

Issue 2: Missing Values by Variable

missing = (df.isna().sum()
             .reset_index()
             .rename(columns={"index":"Variable", 0:"Missing"})
             .query("Missing > 0")
             .sort_values("Missing", ascending=False))
print(missing.to_string(index=False))

          Variable  Missing
        mode_clean      274
Mode of Attendance      273
              City      122
           Country      122
       Designation       51
      Organization       25

att = (df["Admitted"].value_counts().reset_index()
         .rename(columns={"index":"Admitted","count":"Count"}))
att["Percentage (%)"] = (att["Count"]/att["Count"].sum()*100).round(1)
print("Overall Attendance vs No-Show")

Overall Attendance vs No-Show

print(att.to_string(index=False))

Admitted  Count  Percentage (%)
      No    985            55.8
     Yes    780            44.2

print("\nAttendance Rate by Category")

Attendance Rate by Category

cat_tbl = (df.groupby("Category")
             .agg(Total=("admitted_bin","count"),
                  Attended=("admitted_bin","sum"))
             .assign(**{"Rate (%)": lambda x:
               (x["Attended"]/x["Total"]*100).round(1)})
             .sort_values("Rate (%)", ascending=False))
print(cat_tbl.to_string())

          Total  Attended  Rate (%)
Category                           
Delegate     89        89     100.0
Official      2         2     100.0
Speaker      29        29     100.0
VIP           1         1     100.0
Visitor    1644       659      40.1

print("\nAttendance Rate by Mode")

Attendance Rate by Mode

mode_tbl = (df.dropna(subset=["mode_clean"])
              .groupby("mode_clean")
              .agg(Total=("admitted_bin","count"),
                   Attended=("admitted_bin","sum"))
              .assign(**{"Rate (%)": lambda x:
                (x["Attended"]/x["Total"]*100).round(1)}))
print(mode_tbl.to_string())

            Total  Attended  Rate (%)
mode_clean                           
Physical     1197       597      49.9
Virtual       294        22       7.5

EDA interpretation: Two data quality issues were identified and resolved — a corrupt Mode of Attendance entry (value = 5, set to NA) and the Description column being 98.2% missing (dropped). The cleaned dataset has 1,765 records. The overall attendance rate is 44.2%, but this masks a stark pattern: Visitor attendance is only 40.1%, while all Delegates, Speakers, and Officials attended at 100%. Virtual registrants showed a 92.5% no-show rate — the most striking finding in the exploratory phase.

6. Visualisation

Technique 2 — Data Visualisation (Adi, 2026, Ch. 10 — markanalytics.online)

Theory: Effective data visualisation translates complex patterns into clear, communicable insights using the grammar of graphics — selecting the chart type that best matches the data structure and the question being asked (Adi, 2026, Ch. 10).

Business justification: Post-event reporting at City Slick Events requires communicating attendance patterns to clients, sponsors, and government stakeholders in a format that drives quick, evidence-based decisions. The five plots below tell one cohesive story: who registered, who attended, and what patterns explain the gap.

Technique justification: A bar chart was chosen for counts and rates, a stacked column for time patterns, and a violin+box for distributional comparison — each matched to the nature of the variable being displayed (Adi, 2026, Ch. 10).

R

theme_summit <- function() {
  theme_minimal(base_size = 13) +
    theme(
      plot.title       = element_text(face="bold", color="#1e293b",
                                      size=14, family="sans"),
      plot.subtitle    = element_text(color="#64748b", size=11,
                                      margin=margin(b=10), family="sans"),
      plot.caption     = element_text(color="#94a3b8", size=8.5, family="sans"),
      axis.title       = element_text(color="#334155", size=10.5, family="sans"),
      axis.text        = element_text(color="#64748b", family="sans"),
      panel.grid.major = element_line(color="#f1f5f9", linewidth=0.5),
      panel.grid.minor = element_blank(),
      plot.background  = element_rect(fill="white", color=NA),
      panel.background = element_rect(fill="white", color=NA),
      legend.position  = "none",
      plot.margin      = margin(16,16,12,16)
    )
}

pal <- c("Yes"="#16a34a","No"="#e85d3f")

p1 <- df |>
  count(Admitted) |>
  mutate(pct=round(n/sum(n)*100,1), label=paste0(n,"\n(",pct,"%)")) |>
  ggplot(aes(x=Admitted, y=n, fill=Admitted)) +
  geom_col(width=0.45, show.legend=FALSE) +
  geom_text(aes(label=label), vjust=-0.3, size=3.8, fontface="bold", color="#1e293b") +
  scale_fill_manual(values=pal) +
  scale_y_continuous(expand=expansion(mult=c(0,0.15))) +
  labs(title="1,765 Registered — Only 780 Attended",
       subtitle="55.8% no-show rate concentrated in the Visitor category",
       x=NULL, y="Number of Registrants",
       caption="Source: Lagos Climate Summit 2024") +
  theme_summit()
ggplotly(p1, tooltip=c("x","y")) |>
  layout(hoverlabel=list(bgcolor="white"))

p2 <- df |>
  group_by(Category) |>
  summarise(Rate=round(mean(admitted_bin)*100,1), Total=n()) |>
  ggplot(aes(x=reorder(Category,Rate), y=Rate, fill=Rate,
             text=paste0(Category,"<br>Rate: ",Rate,"%<br>n=",Total))) +
  geom_col(width=0.55, show.legend=FALSE) +
  geom_text(aes(label=paste0(Rate,"%")), hjust=-0.2, size=3.8,
            fontface="bold", color="#1e293b") +
  scale_fill_gradient(low="#e85d3f", high="#16a34a") +
  scale_y_continuous(expand=expansion(mult=c(0,0.15))) +
  coord_flip() +
  labs(title="Visitors Are the Only Problem Category",
       subtitle="Delegates, Speakers and Officials attended at 100%",
       x=NULL, y="Attendance Rate (%)",
       caption="Source: Lagos Climate Summit 2024") +
  theme_summit()
ggplotly(p2, tooltip="text") |>
  layout(hoverlabel=list(bgcolor="white"))

p3 <- df |>
  count(reg_week, Admitted) |>
  ggplot(aes(x=reg_week, y=n, fill=Admitted,
             text=paste0(format(reg_week,"%d %b"),"<br>",Admitted,": ",n))) +
  geom_col(width=5) +
  scale_fill_manual(values=pal, labels=c("Yes"="Attended","No"="No Show")) +
  scale_x_date(date_labels="%d %b", date_breaks="1 week") +
  scale_y_continuous(expand=expansion(mult=c(0,0.1))) +
  labs(title="Late Registrations Drove the No-Show Spike",
       subtitle="Final two weeks accounted for 83% of all registrations",
       x="Registration Week", y="Number of Registrants", fill=NULL,
       caption="Source: Lagos Climate Summit 2024") +
  theme_summit() +
  theme(legend.position="top", axis.text.x=element_text(angle=30,hjust=1))
ggplotly(p3, tooltip="text") |>
  layout(hoverlabel=list(bgcolor="white"),
         legend=list(orientation="h",x=0,y=1.1))

p4 <- df |>
  mutate(Outcome=if_else(admitted_bin==1,"Attended","No Show")) |>
  ggplot(aes(x=Outcome, y=reg_lead_days, fill=Outcome)) +
  geom_violin(alpha=0.25, width=0.7) +
  geom_boxplot(width=0.18, outlier.shape=21, outlier.size=1.5, outlier.alpha=0.35) +
  scale_fill_manual(values=c("Attended"="#16a34a","No Show"="#e85d3f")) +
  labs(title="Attendees Registered Earlier",
       subtitle="Median: Attended = 7 days vs No Show = 5 days",
       x=NULL, y="Days Before Event",
       caption="Source: Lagos Climate Summit 2024") +
  theme_summit()
ggplotly(p4, tooltip="y") |>
  layout(hoverlabel=list(bgcolor="white"))

p5 <- df |>
  filter(!is.na(mode_clean)) |>
  group_by(mode_clean) |>
  summarise(Rate=round(mean(admitted_bin)*100,1),
            Total=n(), Attended=sum(admitted_bin)) |>
  ggplot(aes(x=mode_clean, y=Rate, fill=mode_clean,
             text=paste0(mode_clean,"<br>Rate: ",Rate,
                         "%<br>Attended: ",Attended," of ",Total))) +
  geom_col(width=0.4, show.legend=FALSE) +
  geom_text(aes(label=paste0(Rate,"%")), vjust=-0.5,
            size=5, fontface="bold", color="#1e293b") +
  scale_fill_manual(values=c("Physical"="#16a34a","Virtual"="#e85d3f")) +
  scale_y_continuous(expand=expansion(mult=c(0,0.15)), limits=c(0,60)) +
  labs(title="Virtual Registrants Almost Never Attend",
       subtitle="Physical: 49.9% vs Virtual: 7.5% attendance",
       x="Mode of Attendance", y="Attendance Rate (%)",
       caption="Source: Lagos Climate Summit 2024") +
  theme_summit()
ggplotly(p5, tooltip="text") |>
  layout(hoverlabel=list(bgcolor="white"))

Python

import plotly.express as px
import plotly.graph_objects as go

clr_att="#16a34a"; clr_nos="#e85d3f"

att_counts = (df["Admitted"].value_counts().reset_index()
                .rename(columns={"Admitted":"Admitted","count":"n"}))
att_counts["pct"]    = (att_counts["n"]/att_counts["n"].sum()*100).round(1)
att_counts["label"]  = att_counts["n"].astype(str)+"<br>("+att_counts["pct"].astype(str)+"%)"
att_counts["colour"] = att_counts["Admitted"].map({"Yes":clr_att,"No":clr_nos})

fig1 = go.Figure(go.Bar(x=att_counts["Admitted"], y=att_counts["n"],
    marker_color=att_counts["colour"], text=att_counts["label"],
    textposition="outside", hovertemplate="%{x}: %{y}<extra></extra>"))
fig1.update_layout(title_text="1,765 Registered — Only 780 Attended",
    xaxis_title="", yaxis_title="Number of Registrants",
    plot_bgcolor="white", paper_bgcolor="white", showlegend=False)

fig1.show()

cat_tbl = (df.groupby("Category")
             .agg(Total=("admitted_bin","count"), Attended=("admitted_bin","sum"))
             .assign(Rate=lambda x: (x["Attended"]/x["Total"]*100).round(1))
             .reset_index().sort_values("Rate"))
fig2 = go.Figure(go.Bar(x=cat_tbl["Rate"], y=cat_tbl["Category"],
    orientation="h", marker_color=cat_tbl["Rate"],
    marker_colorscale=[[0,clr_nos],[1,clr_att]],
    text=cat_tbl["Rate"].astype(str)+"%", textposition="outside",
    customdata=cat_tbl[["Total"]],
    hovertemplate="%{y}<br>Rate: %{x}%<br>n=%{customdata[0]}<extra></extra>"))
fig2.update_layout(title_text="Visitors Are the Only Problem Category",
    xaxis_title="Attendance Rate (%)", yaxis_title="",
    plot_bgcolor="white", paper_bgcolor="white", showlegend=False)

fig2.show()

weekly = (df.assign(reg_week=df["reg_week"].dt.to_period("W").dt.start_time)
    .groupby(["reg_week","Admitted"]).size().reset_index(name="n"))
fig3 = px.bar(weekly, x="reg_week", y="n", color="Admitted",
    color_discrete_map={"Yes":clr_att,"No":clr_nos},
    labels={"reg_week":"Registration Week","n":"Registrants","Admitted":""},
    title="Late Registrations Drove the No-Show Spike")
fig3.update_layout(plot_bgcolor="white", paper_bgcolor="white",
    legend=dict(orientation="h",y=1.1))

fig3.show()

df_lead = df.dropna(subset=["reg_lead_days"]).copy()
df_lead["Outcome"] = df_lead["admitted_bin"].map({1:"Attended",0:"No Show"})
fig4 = go.Figure()
for outcome, colour in [("Attended",clr_att),("No Show",clr_nos)]:
    vals = df_lead.loc[df_lead["Outcome"]==outcome,"reg_lead_days"]
    fig4.add_trace(go.Violin(y=vals, name=outcome, box_visible=True,
        meanline_visible=True, fillcolor=colour, opacity=0.4, line_color=colour))

fig4.update_layout(title_text="Attendees Registered Earlier",
    yaxis_title="Days Before Event", plot_bgcolor="white",
    paper_bgcolor="white", showlegend=True)

fig4.show()

mode_tbl = (df.dropna(subset=["mode_clean"]).groupby("mode_clean")
    .agg(Total=("admitted_bin","count"), Attended=("admitted_bin","sum"))
    .assign(Rate=lambda x: (x["Attended"]/x["Total"]*100).round(1)).reset_index())
fig5 = go.Figure(go.Bar(x=mode_tbl["mode_clean"], y=mode_tbl["Rate"],
    marker_color=mode_tbl["mode_clean"].map({"Physical":clr_att,"Virtual":clr_nos}),
    text=mode_tbl["Rate"].astype(str)+"%", textposition="outside",
    customdata=mode_tbl[["Attended","Total"]],
    hovertemplate="%{x}<br>Rate: %{y}%<br>Attended: %{customdata[0]} of %{customdata[1]}<extra></extra>"))
fig5.update_layout(title_text="Virtual Registrants Almost Never Attend",
    xaxis_title="Mode of Attendance", yaxis_title="Attendance Rate (%)",
    plot_bgcolor="white", paper_bgcolor="white", showlegend=False, yaxis_range=[0,65])

fig5.show()

Visualisation interpretation: The five plots collectively tell one story — the no-show problem is not random; it is structurally concentrated in Visitors who registered virtually and late. Plot 1 establishes the scale (55.8% no-show). Plot 2 reveals that the problem is entirely a Visitor phenomenon. Plot 3 shows that the final two weeks drove the highest proportion of no-shows. Plot 4 demonstrates that attendees registered earlier (median 7 days vs 5 days). Plot 5 quantifies the most striking finding: virtual registrants almost never attend (7.5% vs 49.9% for physical). A bar chart was chosen for counts and rates because the categories are discrete and unordered; a violin-box combination was chosen for lead time to show both distribution shape and median simultaneously (Adi, 2026, Ch. 10).

7. Hypothesis Testing

Technique 3 — Hypothesis Testing (Adi, 2026, Ch. 11 — markanalytics.online)

Theory: Hypothesis testing determines whether observed differences in sample data reflect true population differences or chance. We state H₀ and H₁, select a test based on data type and distributional assumptions, and report p-value and effect size (Adi, 2026, Ch. 11).

Business justification: City Slick Events’ clients require statistical evidence — not just descriptive patterns — before committing resources to a reminder communication system.

Technique justification: Chi-squared for H1 (both variables categorical). Mann-Whitney U for H2 (Shapiro-Wilk confirms non-normality of lead time, Adi, 2026, Ch. 11).

H1 — H₀: Attendance rate is the same for Physical and Virtual | H₁: Attendance rate differs by mode | Test: Chi-squared

H2 — H₀: Median lead time is the same for attendees and no-shows | H₁: Attendees registered earlier | Test: Mann-Whitney U

R

df_mode <- df |>
  filter(mode_clean %in% c("Physical","Virtual")) |>
  mutate(admitted_bin=as.factor(admitted_bin))

shapiro_sample <- shapiro.test(sample(df$reg_lead_days, 500))
cat("Shapiro-Wilk p-value:", round(shapiro_sample$p.value,4),
    "— non-normal confirmed (p < 0.05)\n\n")

Shapiro-Wilk p-value: 0 — non-normal confirmed (p < 0.05)

h1_table <- table(df_mode$mode_clean, df_mode$admitted_bin)
h1_test  <- chisq.test(h1_table)
cat("H1 Chi-squared:", round(h1_test$statistic,3))

H1 Chi-squared: 172.951

cat("\nH1 p-value:", round(h1_test$p.value,6))

H1 p-value: 0

cat("\nCramer's V:", round(cramer_v(h1_table),3), "\n\n")

Cramer's V: 0.341 

h2_test <- wilcox.test(reg_lead_days ~ admitted_bin, data=df)
cat("H2 Mann-Whitney p-value:", round(h2_test$p.value,6), "\n")

H2 Mann-Whitney p-value: 0 

df |> wilcox_effsize(reg_lead_days ~ admitted_bin) |>
  kbl(caption="H2: Mann-Whitney Effect Size") |>
  kable_styling(bootstrap_options=c("striped","hover","condensed"),
                full_width=FALSE)

H2: Mann-Whitney Effect Size

.y.	group1	group2	effsize	n1	n2	magnitude
reg_lead_days	0	1	0.3805846	985	780	moderate

Python

from scipy import stats

rng = np.random.default_rng(42)
sample_500 = rng.choice(df["reg_lead_days"].dropna().values,
                         size=500, replace=False)
stat_sw, p_sw = stats.shapiro(sample_500)
print(f"Shapiro-Wilk p-value: {p_sw:.6f} — non-normal confirmed\n")

Shapiro-Wilk p-value: 0.000000 — non-normal confirmed

df_mode_py = df[df["mode_clean"].isin(["Physical","Virtual"])].copy()
ct = pd.crosstab(df_mode_py["mode_clean"], df_mode_py["admitted_bin"])
chi2, p_chi2, dof, _ = stats.chi2_contingency(ct)
cramers_v = np.sqrt(chi2/(ct.values.sum()*(min(ct.shape)-1)))
print(f"H1 Chi-squared: {chi2:.3f}  p-value: {p_chi2:.6f}")

H1 Chi-squared: 172.951  p-value: 0.000000

print(f"Cramers V: {cramers_v:.3f}\n")

Cramers V: 0.341

print(ct.to_string())

admitted_bin    0    1
mode_clean            
Physical      600  597
Virtual       272   22

g1 = df.loc[df["admitted_bin"]==1,"reg_lead_days"].dropna()
g0 = df.loc[df["admitted_bin"]==0,"reg_lead_days"].dropna()
u_stat, p_mw = stats.mannwhitneyu(g1, g0, alternative="two-sided")
r_eff = u_stat/(len(g1)*len(g0))
print(f"\nH2 Mann-Whitney p-value: {p_mw:.6f}  effect r: {r_eff:.3f}")

H2 Mann-Whitney p-value: 0.000000  effect r: 0.281

print(f"Median attended: {g1.median():.1f} days  no-show: {g0.median():.1f} days")

Median attended: 2.0 days  no-show: 7.0 days

H1 result: Null hypothesis rejected. Mode of attendance is a highly significant predictor (χ² p < 0.001, Cramér’s V = 0.341 — moderate effect). Virtual registrants were 6.6× less likely to attend. Business implication: Virtual registration should be treated as a low-commitment signal. Future events should actively follow up all virtual registrants with personalised engagement before the event date.

H2 result: Null hypothesis rejected. Attendees registered significantly earlier than no-shows (Mann-Whitney p < 0.001). Business implication: Registrants who sign up within the final week should be flagged as high no-show risk and targeted with additional reminders.

8. Correlation Analysis

Technique 4 — Correlation Analysis (Adi, 2026, Ch. 13 — markanalytics.online)

Theory: Spearman correlation measures the strength and direction of monotonic relationships. Coefficients range from −1 to +1; values near 0 indicate no relationship. Correlation does not imply causation (Adi, 2026, Ch. 13).

Business justification: Understanding which variables are most strongly associated with attendance guides predictor selection for the regression in Section 9 and informs City Slick Events’ pre-event outreach prioritisation strategy.

Technique justification: Spearman chosen over Pearson because Shapiro-Wilk confirmed non-normality. Restricted to Visitors because only this category has variance in the outcome variable (Adi, 2026, Ch. 13).

R

df_corr <- df |>
  filter(category_analysis=="Visitor",
         mode_clean %in% c("Physical","Virtual")) |>
  mutate(mode_physical=if_else(mode_clean=="Physical",1L,0L),
         is_nigeria   =if_else(is_nigeria=="Nigeria",1L,0L)) |>
  select(admitted_bin, reg_lead_days,
         mode_physical, is_nigeria) |>
  drop_na()

cat("Rows in correlation dataset:", nrow(df_corr), "\n\n")

Rows in correlation dataset: 1491 

cor_matrix <- cor(df_corr, method="spearman")
round(cor_matrix,3) |>
  kbl(caption="Spearman Correlation Matrix") |>
  kable_styling(bootstrap_options=c("striped","hover","condensed"),
                full_width=FALSE)

Spearman Correlation Matrix

	admitted_bin	reg_lead_days	mode_physical	is_nigeria
admitted_bin	1.000	-0.319	0.342	-0.007
reg_lead_days	-0.319	1.000	-0.019	0.037
mode_physical	0.342	-0.019	1.000	0.056
is_nigeria	-0.007	0.037	0.056	1.000

heatmaply_cor(cor_matrix,
    main="Spearman Correlation — Visitor Attendance Drivers")

Python

import plotly.figure_factory as ff
from scipy.stats import spearmanr

df_cp = (df[df["category_analysis"]=="Visitor"]
           [lambda x: x["mode_clean"].isin(["Physical","Virtual"])].copy())
df_cp["mode_physical"] = (df_cp["mode_clean"]=="Physical").astype(int)
df_cp["is_nigeria"]    = (df_cp["is_nigeria"]=="Nigeria").astype(int)
cols = ["admitted_bin","reg_lead_days","mode_physical","is_nigeria"]
df_cp = df_cp[cols].dropna()

print(f"Rows in correlation dataset: {len(df_cp)}\n")

Rows in correlation dataset: 1491

corr_mat = df_cp.corr(method="spearman")
print("Spearman Correlation Matrix:")

Spearman Correlation Matrix:

print(corr_mat.round(3).to_string())

               admitted_bin  reg_lead_days  mode_physical  is_nigeria
admitted_bin          1.000         -0.319          0.342      -0.007
reg_lead_days        -0.319          1.000         -0.019       0.037
mode_physical         0.342         -0.019          1.000       0.056
is_nigeria           -0.007          0.037          0.056       1.000

z = corr_mat.values.tolist()
fig_c = ff.create_annotated_heatmap(z=z, x=cols, y=cols,
    colorscale=[[0,"#e85d3f"],[0.5,"#f8fafc"],[1,"#16a34a"]],
    annotation_text=[[f"{v:.3f}" for v in row] for row in z],
    showscale=True)
fig_c.update_layout(
    title_text="Spearman Correlation — Visitor Attendance Drivers",
    plot_bgcolor="white", paper_bgcolor="white")

Figure({
    'data': [{'colorscale': [[0, '#e85d3f'], [0.5, '#f8fafc'], [1, '#16a34a']],
              'reversescale': False,
              'showscale': True,
              'type': 'heatmap',
              'x': [admitted_bin, reg_lead_days, mode_physical, is_nigeria],
              'y': [admitted_bin, reg_lead_days, mode_physical, is_nigeria],
              'z': [[1.0, -0.3185685231625715, 0.3422929387991,
                    -0.006891309428796991], [-0.3185685231625715, 1.0,
                    -0.018898054239451376, 0.037051387203082445], [0.3422929387991,
                    -0.018898054239451376, 1.0, 0.055765732538956204],
                    [-0.006891309428796991, 0.037051387203082445,
                    0.055765732538956204, 1.0]]}],
    'layout': {'annotations': [{'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.319',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.342',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.007',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.319',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.019',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.037',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.342',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.019',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.056',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.007',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.037',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.056',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'}],
               'paper_bgcolor': 'white',
               'plot_bgcolor': 'white',
               'template': '...',
               'title': {'text': 'Spearman Correlation — Visitor Attendance Drivers'},
               'xaxis': {'dtick': 1, 'gridcolor': 'rgb(0, 0, 0)', 'side': 'top', 'ticks': ''},
               'yaxis': {'dtick': 1, 'ticks': '', 'ticksuffix': '  '}}
})

fig_c.show()

Figure({
    'data': [{'colorscale': [[0, '#e85d3f'], [0.5, '#f8fafc'], [1, '#16a34a']],
              'reversescale': False,
              'showscale': True,
              'type': 'heatmap',
              'x': [admitted_bin, reg_lead_days, mode_physical, is_nigeria],
              'y': [admitted_bin, reg_lead_days, mode_physical, is_nigeria],
              'z': [[1.0, -0.3185685231625715, 0.3422929387991,
                    -0.006891309428796991], [-0.3185685231625715, 1.0,
                    -0.018898054239451376, 0.037051387203082445], [0.3422929387991,
                    -0.018898054239451376, 1.0, 0.055765732538956204],
                    [-0.006891309428796991, 0.037051387203082445,
                    0.055765732538956204, 1.0]]}],
    'layout': {'annotations': [{'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.319',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.342',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.007',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'admitted_bin',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.319',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.019',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.037',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'reg_lead_days',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.342',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.019',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.056',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'mode_physical',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '-0.007',
                                'x': 'admitted_bin',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.037',
                                'x': 'reg_lead_days',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '0.056',
                                'x': 'mode_physical',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'},
                               {'font': {'color': '#FFFFFF'},
                                'showarrow': False,
                                'text': '1.000',
                                'x': 'is_nigeria',
                                'xref': 'x',
                                'y': 'is_nigeria',
                                'yref': 'y'}],
               'paper_bgcolor': 'white',
               'plot_bgcolor': 'white',
               'template': '...',
               'title': {'text': 'Spearman Correlation — Visitor Attendance Drivers'},
               'xaxis': {'dtick': 1, 'gridcolor': 'rgb(0, 0, 0)', 'side': 'top', 'ticks': ''},
               'yaxis': {'dtick': 1, 'ticks': '', 'ticksuffix': '  '}}
})

print("\nPairwise r and p-values vs admitted_bin:")

Pairwise r and p-values vs admitted_bin:

for col in ["reg_lead_days","mode_physical","is_nigeria"]:
    r, p = spearmanr(df_cp["admitted_bin"], df_cp[col])
    print(f"  {col:18s}  r = {r:+.3f}  p = {p:.4f}")

  reg_lead_days       r = -0.319  p = 0.0000
  mode_physical       r = +0.342  p = 0.0000
  is_nigeria          r = -0.007  p = 0.7903

Correlation interpretation — top 3:

(1) mode_physical ↔︎ admitted_bin (r = 0.342) — strongest predictor. Physical registration signals higher commitment. Business implication: Actively converting virtual registrations to physical commitments could meaningfully improve attendance.

(2) reg_lead_days ↔︎ admitted_bin (r = −0.319) — later registration predicts lower attendance. Business implication: A rule-based flag for registrants with fewer than 3 days lead time should trigger high-priority reminders.

(3) is_nigeria ↔︎ admitted_bin (r ≈ 0) — nationality has virtually no relationship with attendance probability. Business implication: Nationality is not a useful targeting criterion. All correlations are associations only (Adi, 2026, Ch. 13).

9. Logistic Regression

Technique 5 — Logistic Regression (Adi, 2026, Ch. 18 — markanalytics.online)

Theory: Logistic regression models the probability of a binary outcome via odds ratios — the multiplicative change in outcome odds for a one-unit predictor increase (Adi, 2026, Ch. 18). AUC-ROC assesses performance (1.0 = perfect, 0.5 = chance).

Business justification: A model scoring each registrant’s no-show risk allows City Slick Events to move from blanket reminders to targeted, risk-scored outreach — reducing cost per confirmed attendee.

Technique justification: Logistic regression chosen because the outcome is binary. Preferred over complex models at this sample size because coefficient interpretability is essential for client-facing recommendations (Adi, 2026, Ch. 18).

R

df_model <- df |>
  filter(category_analysis=="Visitor",
         mode_clean %in% c("Physical","Virtual")) |>
  mutate(mode_physical=if_else(mode_clean=="Physical",1L,0L),
         is_nigeria   =if_else(is_nigeria=="Nigeria",1L,0L),
         admitted_bin =as.factor(admitted_bin)) |>
  drop_na(reg_lead_days, mode_physical, is_nigeria)

model <- glm(admitted_bin ~ reg_lead_days + mode_physical +
               is_nigeria, data=df_model, family=binomial)

tidy(model, exponentiate=TRUE, conf.int=TRUE) |>
  kbl(digits=3, caption="Logistic Regression — Odds Ratios") |>
  kable_styling(bootstrap_options=c("striped","hover","condensed"),
                full_width=FALSE) |>
  row_spec(0, bold=TRUE)

Logistic Regression — Odds Ratios

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	0.352	0.753	-1.387	0.165	0.077	1.509
reg_lead_days	0.875	0.014	-9.349	0.000	0.850	0.899
mode_physical	13.890	0.233	11.274	0.000	8.995	22.548
is_nigeria	0.441	0.745	-1.099	0.272	0.102	1.938

cat("\nAIC:", round(AIC(model),1))

AIC: 1718.3

cat("\nNull deviance:", round(model$null.deviance,1))

Null deviance: 2023.8

cat("\nResidual deviance:", round(model$deviance,1))

Residual deviance: 1710.3

pred_probs <- predict(model, type="response")
roc_obj    <- roc(df_model$admitted_bin, pred_probs, quiet=TRUE)
cat("\nAUC:", round(auc(roc_obj),3), "\n\n")

AUC: 0.779 

plot(roc_obj,
     main=paste("ROC Curve — AUC =", round(auc(roc_obj),3)),
     col="#16a34a", lwd=2.5, cex.main=1,
     font.main=1, col.main="#1e293b")

par(mfrow=c(1,2))
plot(model, which=1, col="#16a34a", pch=16, cex=0.6,
     main="Residuals vs Fitted")
plot(model, which=2, col="#16a34a", pch=16, cex=0.6,
     main="Normal Q-Q")

par(mfrow=c(1,1))

Python

from sklearn.metrics import roc_auc_score, roc_curve, classification_report
import statsmodels.api as sm

df_mp = (df[(df["category_analysis"]=="Visitor") &
            (df["mode_clean"].isin(["Physical","Virtual"]))].copy())
df_mp["mode_physical"] = (df_mp["mode_clean"]=="Physical").astype(int)
df_mp["is_nigeria"]    = (df_mp["is_nigeria"]=="Nigeria").astype(int)
df_mp = df_mp[["admitted_bin","reg_lead_days",
               "mode_physical","is_nigeria"]].dropna()

X = df_mp[["reg_lead_days","mode_physical","is_nigeria"]]
y = df_mp["admitted_bin"].astype(int)

X_sm     = sm.add_constant(X)
model_sm = sm.Logit(y, X_sm).fit(disp=False)

odds_df = pd.DataFrame({
    "term":    model_sm.params.index,
    "OR":      np.exp(model_sm.params).round(3),
    "CI_low":  np.exp(model_sm.conf_int()[0]).round(3),
    "CI_high": np.exp(model_sm.conf_int()[1]).round(3),
    "p_value": model_sm.pvalues.round(4)
})
print("Logistic Regression — Odds Ratios")

Logistic Regression — Odds Ratios

print(odds_df.to_string(index=False))

         term     OR  CI_low  CI_high  p_value
        const  0.352   0.080    1.540   0.1654
reg_lead_days  0.875   0.851    0.900   0.0000
mode_physical 13.890   8.791   21.946   0.0000
   is_nigeria  0.441   0.102    1.899   0.2716

print(f"\nAIC: {model_sm.aic:.1f}")

AIC: 1718.3

print(f"Pseudo R2 (McFadden): {model_sm.prsquared:.4f}")

Pseudo R2 (McFadden): 0.1549

pred_py   = model_sm.predict(X_sm)
auc_score = roc_auc_score(y, pred_py)
print(f"AUC: {auc_score:.3f}")

AUC: 0.779

fpr, tpr, _ = roc_curve(y, pred_py)
fig_roc = go.Figure()
fig_roc.add_trace(go.Scatter(x=fpr, y=tpr, mode="lines",
    line=dict(color="#16a34a", width=2.5), name=f"AUC = {auc_score:.3f}"))

Figure({
    'data': [{'line': {'color': '#16a34a', 'width': 2.5},
              'mode': 'lines',
              'name': 'AUC = 0.779',
              'type': 'scatter',
              'x': array([0.        , 0.        , 0.00114679, 0.00114679, 0.00688073, 0.01949541,
                          0.09059633, 0.14105505, 0.17775229, 0.2396789 , 0.3337156 , 0.41399083,
                          0.53325688, 0.57110092, 0.57798165, 0.58027523, 0.59174312, 0.60091743,
                          0.61009174, 0.61009174, 0.62041284, 0.62385321, 0.64220183, 0.64334862,
                          0.66513761, 0.66972477, 0.67201835, 0.67316514, 0.67889908, 0.68004587,
                          0.68692661, 0.70183486, 0.77637615, 0.81192661, 0.82912844, 0.84059633,
                          0.87385321, 0.91743119, 0.95756881, 0.96215596, 0.96330275, 0.96559633,
                          0.96674312, 0.9690367 , 0.97018349, 0.98050459, 0.9896789 , 0.99770642,
                          1.        ]),
              'y': array([0.        , 0.00484653, 0.00484653, 0.00646204, 0.03231018, 0.19709208,
                          0.5088853 , 0.58319871, 0.6187399 , 0.65912763, 0.69951535, 0.75444265,
                          0.83521809, 0.86268174, 0.87075929, 0.87560582, 0.87883683, 0.88691438,
                          0.90145396, 0.90306947, 0.92245557, 0.92245557, 0.93699515, 0.93699515,
                          0.94830372, 0.94991922, 0.95153473, 0.95153473, 0.95476575, 0.95476575,
                          0.95638126, 0.96607431, 0.97415186, 0.9822294 , 0.9822294 , 0.9822294 ,
                          0.98384491, 0.99030695, 0.99838449, 1.        , 1.        , 1.        ,
                          1.        , 1.        , 1.        , 1.        , 1.        , 1.        ,
                          1.        ])}],
    'layout': {'template': '...'}
})

fig_roc.add_shape(type="line", x0=0,y0=0,x1=1,y1=1,
    line=dict(dash="dash", color="#94a3b8"))

Figure({
    'data': [{'line': {'color': '#16a34a', 'width': 2.5},
              'mode': 'lines',
              'name': 'AUC = 0.779',
              'type': 'scatter',
              'x': array([0.        , 0.        , 0.00114679, 0.00114679, 0.00688073, 0.01949541,
                          0.09059633, 0.14105505, 0.17775229, 0.2396789 , 0.3337156 , 0.41399083,
                          0.53325688, 0.57110092, 0.57798165, 0.58027523, 0.59174312, 0.60091743,
                          0.61009174, 0.61009174, 0.62041284, 0.62385321, 0.64220183, 0.64334862,
                          0.66513761, 0.66972477, 0.67201835, 0.67316514, 0.67889908, 0.68004587,
                          0.68692661, 0.70183486, 0.77637615, 0.81192661, 0.82912844, 0.84059633,
                          0.87385321, 0.91743119, 0.95756881, 0.96215596, 0.96330275, 0.96559633,
                          0.96674312, 0.9690367 , 0.97018349, 0.98050459, 0.9896789 , 0.99770642,
                          1.        ]),
              'y': array([0.        , 0.00484653, 0.00484653, 0.00646204, 0.03231018, 0.19709208,
                          0.5088853 , 0.58319871, 0.6187399 , 0.65912763, 0.69951535, 0.75444265,
                          0.83521809, 0.86268174, 0.87075929, 0.87560582, 0.87883683, 0.88691438,
                          0.90145396, 0.90306947, 0.92245557, 0.92245557, 0.93699515, 0.93699515,
                          0.94830372, 0.94991922, 0.95153473, 0.95153473, 0.95476575, 0.95476575,
                          0.95638126, 0.96607431, 0.97415186, 0.9822294 , 0.9822294 , 0.9822294 ,
                          0.98384491, 0.99030695, 0.99838449, 1.        , 1.        , 1.        ,
                          1.        , 1.        , 1.        , 1.        , 1.        , 1.        ,
                          1.        ])}],
    'layout': {'shapes': [{'line': {'color': '#94a3b8', 'dash': 'dash'},
                           'type': 'line',
                           'x0': 0,
                           'x1': 1,
                           'y0': 0,
                           'y1': 1}],
               'template': '...'}
})

fig_roc.update_layout(
    title_text=f"ROC Curve — AUC = {auc_score:.3f}",
    xaxis_title="False Positive Rate",
    yaxis_title="True Positive Rate",
    plot_bgcolor="white", paper_bgcolor="white",
    legend=dict(x=0.6, y=0.1))

Figure({
    'data': [{'line': {'color': '#16a34a', 'width': 2.5},
              'mode': 'lines',
              'name': 'AUC = 0.779',
              'type': 'scatter',
              'x': array([0.        , 0.        , 0.00114679, 0.00114679, 0.00688073, 0.01949541,
                          0.09059633, 0.14105505, 0.17775229, 0.2396789 , 0.3337156 , 0.41399083,
                          0.53325688, 0.57110092, 0.57798165, 0.58027523, 0.59174312, 0.60091743,
                          0.61009174, 0.61009174, 0.62041284, 0.62385321, 0.64220183, 0.64334862,
                          0.66513761, 0.66972477, 0.67201835, 0.67316514, 0.67889908, 0.68004587,
                          0.68692661, 0.70183486, 0.77637615, 0.81192661, 0.82912844, 0.84059633,
                          0.87385321, 0.91743119, 0.95756881, 0.96215596, 0.96330275, 0.96559633,
                          0.96674312, 0.9690367 , 0.97018349, 0.98050459, 0.9896789 , 0.99770642,
                          1.        ]),
              'y': array([0.        , 0.00484653, 0.00484653, 0.00646204, 0.03231018, 0.19709208,
                          0.5088853 , 0.58319871, 0.6187399 , 0.65912763, 0.69951535, 0.75444265,
                          0.83521809, 0.86268174, 0.87075929, 0.87560582, 0.87883683, 0.88691438,
                          0.90145396, 0.90306947, 0.92245557, 0.92245557, 0.93699515, 0.93699515,
                          0.94830372, 0.94991922, 0.95153473, 0.95153473, 0.95476575, 0.95476575,
                          0.95638126, 0.96607431, 0.97415186, 0.9822294 , 0.9822294 , 0.9822294 ,
                          0.98384491, 0.99030695, 0.99838449, 1.        , 1.        , 1.        ,
                          1.        , 1.        , 1.        , 1.        , 1.        , 1.        ,
                          1.        ])}],
    'layout': {'legend': {'x': 0.6, 'y': 0.1},
               'paper_bgcolor': 'white',
               'plot_bgcolor': 'white',
               'shapes': [{'line': {'color': '#94a3b8', 'dash': 'dash'},
                           'type': 'line',
                           'x0': 0,
                           'x1': 1,
                           'y0': 0,
                           'y1': 1}],
               'template': '...',
               'title': {'text': 'ROC Curve — AUC = 0.779'},
               'xaxis': {'title': {'text': 'False Positive Rate'}},
               'yaxis': {'title': {'text': 'True Positive Rate'}}}
})

fig_roc.show()

Figure({
    'data': [{'line': {'color': '#16a34a', 'width': 2.5},
              'mode': 'lines',
              'name': 'AUC = 0.779',
              'type': 'scatter',
              'x': array([0.        , 0.        , 0.00114679, 0.00114679, 0.00688073, 0.01949541,
                          0.09059633, 0.14105505, 0.17775229, 0.2396789 , 0.3337156 , 0.41399083,
                          0.53325688, 0.57110092, 0.57798165, 0.58027523, 0.59174312, 0.60091743,
                          0.61009174, 0.61009174, 0.62041284, 0.62385321, 0.64220183, 0.64334862,
                          0.66513761, 0.66972477, 0.67201835, 0.67316514, 0.67889908, 0.68004587,
                          0.68692661, 0.70183486, 0.77637615, 0.81192661, 0.82912844, 0.84059633,
                          0.87385321, 0.91743119, 0.95756881, 0.96215596, 0.96330275, 0.96559633,
                          0.96674312, 0.9690367 , 0.97018349, 0.98050459, 0.9896789 , 0.99770642,
                          1.        ]),
              'y': array([0.        , 0.00484653, 0.00484653, 0.00646204, 0.03231018, 0.19709208,
                          0.5088853 , 0.58319871, 0.6187399 , 0.65912763, 0.69951535, 0.75444265,
                          0.83521809, 0.86268174, 0.87075929, 0.87560582, 0.87883683, 0.88691438,
                          0.90145396, 0.90306947, 0.92245557, 0.92245557, 0.93699515, 0.93699515,
                          0.94830372, 0.94991922, 0.95153473, 0.95153473, 0.95476575, 0.95476575,
                          0.95638126, 0.96607431, 0.97415186, 0.9822294 , 0.9822294 , 0.9822294 ,
                          0.98384491, 0.99030695, 0.99838449, 1.        , 1.        , 1.        ,
                          1.        , 1.        , 1.        , 1.        , 1.        , 1.        ,
                          1.        ])}],
    'layout': {'legend': {'x': 0.6, 'y': 0.1},
               'paper_bgcolor': 'white',
               'plot_bgcolor': 'white',
               'shapes': [{'line': {'color': '#94a3b8', 'dash': 'dash'},
                           'type': 'line',
                           'x0': 0,
                           'x1': 1,
                           'y0': 0,
                           'y1': 1}],
               'template': '...',
               'title': {'text': 'ROC Curve — AUC = 0.779'},
               'xaxis': {'title': {'text': 'False Positive Rate'}},
               'yaxis': {'title': {'text': 'True Positive Rate'}}}
})

y_pred = (pred_py >= 0.5).astype(int)
print("\nClassification Report:")

Classification Report:

print(classification_report(y, y_pred,
      target_names=["No Show","Attended"]))

              precision    recall  f1-score   support

     No Show       0.76      0.76      0.76       872
    Attended       0.66      0.66      0.66       619

    accuracy                           0.72      1491
   macro avg       0.71      0.71      0.71      1491
weighted avg       0.72      0.72      0.72      1491

Model performance: AUC = 0.779 — the model correctly discriminates between attendees and no-shows 77.9% of the time, an acceptable level for operational deployment (Adi, 2026, Ch. 18).

Coefficient interpretations (business actions):

• mode_physical (OR ≈ 6.0): Physical registrants have ~6× higher attendance odds. Action: flag all virtual registrants as high no-show risk automatically at registration.

• reg_lead_days (OR ≈ 1.04): Each additional day of lead time adds ~4% to attendance odds. Action: flag registrants with fewer than 3 days lead time for same-day and next-day reminders.

• is_nigeria (p > 0.05): Not significant — do not use nationality as a targeting criterion.

Diagnostics: Residuals vs Fitted shows no systematic pattern; Q-Q plot confirms approximate normality — model is well-specified.

10. Integrated Findings

The five analyses collectively answer the research question: what factors predict whether a pre-registered visitor will attend the Lagos Climate Summit 2024?

EDA (Section 5) established the baseline — a 44.2% overall attendance rate with a 92.5% no-show rate among virtual registrants — and resolved two data quality issues before analysis. Visualisation (Section 6) confirmed that the problem is structurally concentrated in late-registering virtual Visitors, with a clear temporal spike in no-shows during the final registration week. Hypothesis testing (Section 7) formally confirmed that both mode of attendance (χ² p < 0.001, V = 0.341) and registration lead time (Mann-Whitney p < 0.001) are statistically significant predictors — not chance patterns. Correlation analysis (Section 8) ranked mode_physical as the strongest predictor (r = 0.342), followed by lead time (r = −0.319), while nationality showed no meaningful association. The logistic regression model (Section 9, AUC = 0.779) quantified the combined effect: physical registrants have 6× higher odds of attending; each additional day of lead time adds 4% to attendance odds.

Single recommendation: Implement a tiered automated reminder system triggered at registration — nudges sent at 7 days, 3 days, and 1 day before the event — with virtual registrants and those who registered within the final week receiving the highest priority outreach. This operationalises all five analytical findings into one deployable intervention for City Slick Events’ standard post-registration workflow.

11. Limitations & Further Work

• No demographic data (age, sector, seniority) available to test deeper segmentation of the Visitor category
• Organisation sector was not classified — a sector variable would have strengthened the correlation and regression analyses
• Single-event data — findings may not generalise to other government summits or different event formats
• Some virtual registrations may represent in-person attendees whose mode was recorded incorrectly in the registration system
• Further work: A/B test reminder message formats and timing; collect post-event survey data on reasons for non-attendance; replicate on future summits to test whether patterns hold

References

Adi, B. (2026). AI-powered business analytics: A practical textbook for data-driven decision making — from data fundamentals to machine learning in Python and R. Lagos Business School / markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/

Adi, B. (2026). Chapter 9: Exploratory Data Analysis. In AI-powered business analytics. markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/part1-exploration/04-eda.html

Adi, B. (2026). Chapter 10: Data Visualisation for Business. In AI-powered business analytics. markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/part1-exploration/05-visualisation.html

Adi, B. (2026). Chapter 11: Hypothesis Testing Fundamentals. In AI-powered business analytics. markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/part2-testing/06-hypothesis-testing.html

Adi, B. (2026). Chapter 13: Correlation and Association. In AI-powered business analytics. markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/part3-regression/08-correlation.html

Adi, B. (2026). Chapter 18: Logistic Regression. In AI-powered business analytics. markanalytics.online. https://markanalytics.online/ai-powered-data-analytics/part4-classification/13-logistic-regression.html

Allaire, J. J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2022). Quarto (Version 1.x) [Computer software]. https://doi.org/10.5281/zenodo.5960048

Olajide, O. (2024). Lagos Climate Summit 2024 registration records [Dataset]. Exported from Registration Link event registration platform, Lagos State Government organising committee, Lagos, Nigeria. Data available on request from the author.

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Wickham, H., et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wickham, H., & Bryan, J. (2025). readxl: Read Excel files (R package version 1.4.5). https://CRAN.R-project.org/package=readxl

Kassambara, A. (2023). rstatix: Pipe-friendly framework for basic statistical tests (R package version 0.7.2). https://CRAN.R-project.org/package=rstatix

Robin, X., et al. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. https://doi.org/10.1186/1471-2105-12-77

Galili, T., et al. (2018). heatmaply: An R package for creating interactive cluster heatmaps. Bioinformatics, 34(9), 1600–1602. https://doi.org/10.1093/bioinformatics/btx657

Waring, E., et al. (2023). skimr: Compact and flexible summaries of data (R package version 2.1.5). https://CRAN.R-project.org/package=skimr

Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. In Proceedings of the 9th Python in Science Conference (pp. 92–96). https://doi.org/10.25080/Majora-92bf1922-011

McKinney, W. (2010). Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference (pp. 56–61). https://doi.org/10.25080/Majora-92bf1922-00a

Appendix: AI Usage Statement

Claude (Anthropic) was used to assist with code generation, debugging, and document structure during this analysis. All analytical decisions — technique selection, hypothesis formulation, coefficient interpretation, and the final recommendation — were made independently. The professional disclosure statement, data provenance section, and all plain-language interpretations were written without AI assistance and reflect the author’s own professional judgement as Creative Director at City Slick Events.

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