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appDS
Summary
Clean Data
Univariate
Distribution
Outliers
Bivariate
Segmentation
Time Series
Clustering
Classification
Forecasting
Deep Chat
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Dark Mode
C
demo
business
Report
Live Stream
LIVE STREAM
205 rows
4 features
43 ms
Throughput
258.0MB/s
Latency
14ms
Quality
99.8%
Data Quality
Before → After Cleaning
Outlier Detection
8 Anomalies Found
Pattern Discovery
Hidden Clusters Found
Model Accuracy
Training Progress
Data Cleaning
Ready
Drop Columns
Rename Cols
Fill Missing
Filter Rows
Select Columns to Drop
customer_id
transaction_date
amount
currency
region
referral_code
device_type
session_duration
Drop (1)
Univariate
Ready
Mean
32.45
Median
33.53
Std Dev
4.12
Transaction_ValueNumeric
0.00500.001000.00
Distribution Analysis
Ready
Gaussian Fit Test
Column: Age_Demographic
Passed (p > 0.05)
184580+
Outlier Detection
Ready
Mean
425.20
Std Dev
12.5
Anomaly
3 Found
IDValueZ-Score
Row 8429,203.4+4.2σ
Row 10512.1-3.8σ
Bivariate Analysis
Ready
X-AXISMarketing_Spend
vs
Y-AXISRevenue_Q4
Pearson Correlation
+0.87
Customer Segmentation
Ready
3
Groups
High Value
Freq > 5/mo
45%
Loyalists
Tenure > 2yr
30%
At Risk
No Activity 30d
25%
Time Series Analysis
Ready
Decomposition: Multiplicative
Original
Trend
Seasonality
K-Means Clustering
Ready
3
✓
Income✓
Spend_Score✓
Age✓
TenureInertia: 452.1
Classification
Ready
Model
Accuracy
Accuracy
98.2%
F1 Score
0.97
Confusion Matrix
True Positive
False Positive
False Negative
True Negative
Predictive Forecasting
Ready
ARIMA
PROPHET
Forecast (30d)
+24.5%
High Confidence
Deep Chat Assistant
Ready
Agent Online
I've analyzed your forecasting model. The ARIMA parameters suggest a strong seasonal component in Q4. Would you like to adjust the seasonality prior?
You
Yes, set seasonality to 12 months and run the simulation again.
Running Simulation...
Type a message...
Highly abstracted and simplified demonstration of the actual application