🧬 Exploratory Data Analysis

African Gig-Economy and Digital Wallet Risk

Generated on 2026-07-10 15:51:17

📊 Dataset Overview

5
Tables
55,746
Total Rows
48
Total Columns
14.87 MB
Memory Usage

Tables Summary

Table Name Rows Columns Numeric Categorical Missing %
dim_worker 5,000 10 2 8 0.0%
dim_date 730 9 2 7 0.0%
dim_market 4 8 2 6 0.0%
dim_channel 12 6 1 5 0.0%
fact_transactions 50,000 15 5 10 0.0%

📋 dim_worker

5,000 rows × 10 columns

📈 Numeric Column Statistics

Column Mean Std Min 25% 50% 75% Max
account_tenure_days 906.62 525.75 0.00 458.75 909.50 1358.25 1825.00
risk_score 50.43 29.15 0.00 25.01 50.98 75.61 99.96

🏷️ Categorical Column Statistics

Column Unique Values Most Common Frequency
worker_id 5000 val_0 1
worker_name 40 Sipho Dlamini 146
gig_segment 15 Freelance Creative 368
kyc_tier 4 Tier 2 (ID Verified) 1,285
gender 4 Prefer Not to Say 1,274
age_band 5 55+ 1,019
is_active 2 False 2,559
preferred_channel 6 Mobile App (iOS) 861

📉 Numeric Distributions

📊 Categorical Distributions

🔗 Correlation Matrix

📋 dim_date

730 rows × 9 columns

📈 Numeric Column Statistics

Column Mean Std Min 25% 50% 75% Max
month 6.50 3.44 1.00 4.00 6.50 10.00 12.00
week_number 27.57 15.37 1.00 15.00 27.50 41.00 53.00

🏷️ Categorical Column Statistics

Column Unique Values Most Common Frequency
date_id 1 DERIVE_FROM_full_date: format 730
full_date 578 2021-11-15 4
year 1 DERIVE_FROM_full_date: extract 730
quarter 1 DERIVE_FROM_full_date: Q1 if m 730
day_of_week 7 Thursday 118
is_weekend 1 DERIVE_FROM_full_date: True if 730
is_month_end 1 DERIVE_FROM_full_date: True on 730

📉 Numeric Distributions

📊 Categorical Distributions

🔗 Correlation Matrix

📋 dim_market

4 rows × 8 columns

📈 Numeric Column Statistics

Column Mean Std Min 25% 50% 75% Max
usd_fx_rate 418.29 330.31 80.23 285.89 360.26 492.66 872.42
market_fraud_index 469.87 228.44 267.52 361.52 407.22 515.58 797.51

🏷️ Categorical Column Statistics

Column Unique Values Most Common Frequency
market_id 4 val_0 1
country 2 South Africa 3
iso_code 3 ZA 2
local_currency 1 GHS 4
dominant_channel 3 Mobile Money Agent 2
regulatory_tier 2 Mature (Tier 1) 3

📉 Numeric Distributions

📊 Categorical Distributions

🔗 Correlation Matrix

📋 dim_channel

12 rows × 6 columns

📈 Numeric Column Statistics

Column Mean Std Min 25% 50% 75% Max
avg_fraud_rate 574.49 170.91 156.68 516.75 579.63 678.91 857.61

🏷️ Categorical Column Statistics

Column Unique Values Most Common Frequency
channel_id 12 val_0 1
channel_type 5 API/Third-Party 3
channel_subtype 7 API Integration 3
is_digital 2 True 6
requires_internet 2 False 8

📉 Numeric Distributions

📊 Categorical Distributions

📋 fact_transactions

50,000 rows × 15 columns

📈 Numeric Column Statistics

Column Mean Std Min 25% 50% 75% Max
amount_local 499.75 289.36 0.02 248.34 498.15 751.43 1000.00
amount_usd 499.51 288.07 0.02 249.62 498.74 748.61 999.97
velocity_score 499.16 289.08 0.00 247.93 499.99 748.63 999.97
fraud_loss_usd 498.37 288.35 0.01 249.06 496.82 748.73 999.99
processing_time_ms 500.34 288.60 0.00 251.00 499.00 751.00 1000.00

🏷️ Categorical Column Statistics

Column Unique Values Most Common Frequency
transaction_id 50000 val_0 1
worker_id 4999 val_2210 25
date_id 1 DERIVE_FROM_full_date: format 50,000
channel_id 12 val_11 4,258
market_id 4 val_2 12,629
transaction_type 13 Wallet Funding via Card 3,924
transaction_outcome 8 Completed 6,319
is_fraud_flagged 2 True 25,145
is_disputed 2 True 25,035
is_reversed 2 False 25,048

📉 Numeric Distributions

📊 Categorical Distributions

🔗 Correlation Matrix