instruction stringlengths 16 55 | input dict | output stringlengths 573 7.08k |
|---|---|---|
Analyze loyalty score city | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
62.13,
111.4,
87.11,
154.16,
107.24,
146.92,
101.84,
175.28,
118.98,
196.76,
62.21,
133.22,
51.67,
129.18,
53.01,
85.66,
193.85,
114.23,
160.13,
162.11,
69.25,
96.35,... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Kelompokkan pendapatan wilayah | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
198.79,
196.59,
57.68,
123.24,
134.05,
137.3,
191.65,
179,
141.71,
55.59,
190.14,
142.75,
112.47,
85.63,
144.38,
175.87,
100.44,
126.06,
108.54,
63.68,
130.18,
67.88,... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Analisis diskon periode waktu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Berlin",
"São Paulo",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"Germany",
"Brazil",
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Berlin', 'São Paulo', 'Tokyo'], 'Country/Negara': ['France', 'Mexico', 'Germany', 'Brazil', 'Japan'], 'Region/Wilayah': ['Europe', 'North America', ... |
Calculate loyalty score time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [297, 929, 378], 'Price/Harga': [29.16, 46.31, 144.04], 'Category/Kategori': ['Automotive/Otomotif', 'Automotiv... |
Compare growth rate gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tools_2', 'Tires_3'], 'Sales/Penjualan': [803, 723, 775], 'Price/Harga': [107.6, 161.95, 78.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otom... |
Analisis pangsa pasar negara | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
113.75,
144.75,
121.19,
74.37,
91.78,
60.88,
64.61,
96.77,
99.36,
143.42,
83.28,
168.7,
113.59,
98.14,
54.31,
199.02,
108.99,
89.33,
126.97,
138.8,
70.74,
191.31,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Segment discount gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"New York",
"Paris",
"Tokyo",
"London",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"US",
"France",
"Japan",
"UK",
"Australia"
]... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['New York', 'Paris', 'Tokyo', 'London', 'Sydney'], 'Country/Negara': ['US', 'France', 'Japan', 'UK', 'Australia'], 'Region/Wilayah': ['North America', 'Europe', 'Asia', 'Eur... |
Visualisasikan penjualan jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Singapore",
"Berlin",
"Jakarta",
"Dubai",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Singapore",
"Germany",
"Indonesia",
"UAE",
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Singapore', 'Berlin', 'Jakarta', 'Dubai', 'Tokyo'], 'Country/Negara': ['Singapore', 'Germany', 'Indonesia', 'UAE', 'Japan'], 'Region/Wilayah': ['Asia', 'Europe', 'Asia', 'M... |
Identifikasi skor loyalitas jenis kelamin | {
"Age/Usia": [
32,
46,
39,
67,
21,
34,
42,
53,
47,
34,
29,
29,
56,
44,
38,
53,
45,
22,
63,
26,
27,
29,
67,
63,
26,
27,
42,
44,
37,
57,
66,
37,
48,
68,
64,
48,
31,
70... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Segmentasi pertumbuhan grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Mumbai",
"Mexico City",
"London",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"India",
"Mexico",
"UK",
"US"
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Mumbai', 'Mexico City', 'London', 'New York'], 'Country/Negara': ['Japan', 'India', 'Mexico', 'UK', 'US'], 'Region/Wilayah': ['Asia', 'Asia', 'North America', 'Eur... |
Optimize income gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
98.75,
122.42,
167.55,
170.74,
187.8,
181.51,
123.15,
136.72,
96.95,
162.23,
60.25,
87.53,
54.87,
129.71,
162.76,
146.62,
104.05,
105.14,
78.33,
114.94,
74.9,
67.73,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Calculate conversion rate age group | {
"Age/Usia": [
19,
40,
63,
52,
68,
24,
53,
37,
48,
65,
39,
26,
46,
50,
61,
69,
20,
67,
46,
62,
28,
62,
45,
26,
59,
65,
21,
33,
57,
20,
25,
58,
47,
32,
27,
19,
62,
33... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Bandingkan pendapatan kota | {
"Age/Usia": [
54,
53,
68,
42,
53,
56,
22,
42,
19,
40,
66,
68,
18,
18,
39,
64,
70,
53,
56,
23,
64,
45,
47,
32,
33,
61,
23,
22,
21,
50,
19,
19,
64,
35,
32,
66,
59,
68... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Segmentasi pangsa pasar grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Oil_2', 'Tools_3'], 'Sales/Penjualan': [271, 507, 383], 'Price/Harga': [79.74, 69.58, 51.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive... |
Bandingkan pangsa pasar kota | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
172.48,
85.7,
188.02,
97.63,
185.68,
75.64,
130.46,
130.7,
126.33,
145,
96.98,
129.09,
54.07,
145.62,
191.73,
51.63,
119.15,
59.05,
125.72,
124.68,
128.19,
133.96,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Forecast market share for/untuk month | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Jakarta",
"Singapore",
"São Paulo",
"Mumbai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"Indonesia",
"Singapore",
"Brazil",
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Jakarta', 'Singapore', 'São Paulo', 'Mumbai'], 'Country/Negara': ['UK', 'Indonesia', 'Singapore', 'Brazil', 'India'], 'Region/Wilayah': ['Europe', 'Asia', 'Asia',... |
Visualisasikan pengunjung grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Mumbai",
"Sydney",
"Berlin",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"India",
"Australia",
"Germany",
"US"
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Mumbai', 'Sydney', 'Berlin', 'New York'], 'Country/Negara': ['UK', 'India', 'Australia', 'Germany', 'US'], 'Region/Wilayah': ['Europe', 'Asia', 'Oceania', 'Europe... |
Perkiraan pendapatan for/untuk minggu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"New York",
"Dubai",
"Sydney",
"London"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"US",
"UAE",
"Australia",
"UK"
],
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'New York', 'Dubai', 'Sydney', 'London'], 'Country/Negara': ['France', 'US', 'UAE', 'Australia', 'UK'], 'Region/Wilayah': ['Europe', 'North America', 'Middle East',... |
Correlate loyalty score and/dan income | {
"Age/Usia": [
54,
19,
42,
56,
40,
70,
49,
23,
38,
68,
68,
38,
30,
44,
19,
69,
48,
39,
59,
59,
70,
54,
42,
51,
54,
25,
37,
41,
58,
18,
36,
55,
48,
33,
40,
53,
41,
59... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Hitung pangsa pasar produk | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Paris",
"São Paulo",
"Tokyo",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"France",
"Brazil",
"Japan",
"US"
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Paris', 'São Paulo', 'Tokyo', 'New York'], 'Country/Negara': ['India', 'France', 'Brazil', 'Japan', 'US'], 'Region/Wilayah': ['Asia', 'Europe', 'South America', '... |
Analisis pendapatan periode waktu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'TV_2', 'Laptop_3'], 'Sales/Penjualan': [497, 276, 658], 'Price/Harga': [20.56, 11.1, 138.72], 'Category/Kategori': ['Electronics/Elektronik', 'Electron... |
Forecast revenue for/untuk year | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Mumbai",
"New York",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"India",
"US",
"Aust... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Mumbai', 'New York', 'Sydney'], 'Country/Negara': ['France', 'Mexico', 'India', 'US', 'Australia'], 'Region/Wilayah': ['Europe', 'North America', 'A... |
Segment revenue time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [177, 820, 234], 'Price/Harga': [197.94, 177.55, 155.37], 'Category/Kategori': ['Automotive/Otomotif', 'Automot... |
Segmentasi nilai pesanan rata-rata produk | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Fruits_2', 'Snacks_3'], 'Sales/Penjualan': [107, 103, 397], 'Price/Harga': [90.0, 56.0, 73.15], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/... |
Segmentasi pertumbuhan segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
58.28,
97.08,
188.67,
191.48,
137.45,
50.38,
147.9,
161.82,
97.03,
102.29,
108.59,
192.72,
75.99,
115.55,
185.04,
92.28,
152.81,
132.72,
144.53,
168.4,
63.73,
67.32,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Optimalkan nilai pesanan rata-rata negara | {
"Age/Usia": [
64,
68,
69,
66,
62,
51,
38,
20,
33,
70,
42,
21,
36,
25,
28,
43,
63,
51,
29,
19,
67,
43,
37,
49,
55,
41,
36,
33,
60,
60,
48,
25,
40,
36,
63,
56,
53,
68... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Compare average order value city | {
"Age/Usia": [
48,
62,
64,
20,
56,
18,
27,
66,
27,
48,
38,
44,
41,
25,
28,
47,
40,
42,
30,
57,
67,
29,
53,
61,
42,
35,
24,
44,
53,
58,
19,
58,
53,
25,
23,
55,
58,
21... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Identifikasi skor loyalitas jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [868, 428, 790], 'Price/Harga': [93.43, 126.36, 183.55], 'Category/Kategori': ['Automotive/Otomotif', 'Automoti... |
Segment growth rate region | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Berlin",
"Dubai",
"Sydney",
"Singapore",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Germany",
"UAE",
"Australia",
"Singapore",
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Berlin', 'Dubai', 'Sydney', 'Singapore', 'São Paulo'], 'Country/Negara': ['Germany', 'UAE', 'Australia', 'Singapore', 'Brazil'], 'Region/Wilayah': ['Europe', 'Middle East',... |
Perkiraan nilai pesanan rata-rata for/untuk 3 bulan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [476, 233, 993], 'Price/Harga': [39.42, 72.33, 52.47], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomo... |
Optimalkan rasio konversi kategori | {
"Age/Usia": [
67,
39,
58,
64,
69,
69,
70,
53,
61,
26,
57,
58,
41,
52,
26,
38,
21,
19,
54,
47,
23,
23,
60,
52,
45,
49,
66,
34,
59,
36,
25,
62,
57,
55,
21,
22,
62,
47... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Korelasi stok and/dan pengunjung | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
53.35,
104.13,
82.59,
82.67,
82.7,
67.42,
170.61,
166.08,
93.25,
185.35,
197.22,
184.58,
165.21,
70.55,
81.46,
184.71,
155.57,
90.51,
52.58,
178.41,
151.51,
170.83,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Optimalkan skor loyalitas jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
134.42,
121.18,
108.88,
97.22,
102.79,
184.62,
109.87,
101.36,
172.34,
167.19,
155.34,
84.45,
183.35,
140.92,
92.77,
156.45,
107.36,
55.67,
59.39,
78.26,
138.42,
190.... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Analyze growth rate customer segment | {
"Age/Usia": [
66,
34,
56,
69,
24,
60,
34,
21,
25,
57,
44,
24,
45,
24,
19,
60,
38,
26,
21,
39,
33,
18,
59,
25,
54,
36,
68,
63,
28,
63,
56,
22,
65,
22,
43,
67,
29,
47... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Forecast price for/untuk year | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
177.15,
179.1,
89.85,
128.85,
51.62,
66.95,
169.7,
175.1,
130.86,
189.79,
158.87,
131.49,
91.56,
86.42,
169.2,
190.49,
76.62,
169.76,
187.07,
104.54,
188.65,
162.4,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Analyze average order value product | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [246, 971, 721], 'Price/Harga': [41.59, 152.91, 175.27], 'Category/Kategori': ['Electronics/Elektronik', 'Electr... |
Kelompokkan pangsa pasar kategori | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
71.25,
111.18,
85.06,
129.36,
63.01,
187.63,
92.03,
189.45,
131.09,
161.71,
134.77,
128.22,
50.27,
74.57,
120.34,
178.23,
164.19,
171.93,
90.67,
142.15,
199.27,
95.58... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Hitung pendapatan segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Smartphone_2', 'Laptop_3'], 'Sales/Penjualan': [907, 206, 648], 'Price/Harga': [154.13, 126.38, 84.91], 'Category/Kategori': ['Electronics/Elektronik',... |
Bandingkan diskon kota | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tires_1', 'Accessories_2', 'Tools_3'], 'Sales/Penjualan': [699, 105, 121], 'Price/Harga': [193.34, 83.85, 178.17], 'Category/Kategori': ['Automotive/Otomotif', 'Automo... |
Compare discount time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Snacks_2', 'Dairy_3'], 'Sales/Penjualan': [586, 879, 682], 'Price/Harga': [181.55, 183.82, 137.98], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', '... |
Optimalkan pengunjung negara | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
147.47,
109.1,
98.65,
106.21,
89.72,
190.1,
198.44,
110.11,
174.24,
157.71,
198.41,
83.41,
131.77,
128.38,
140.52,
62.34,
170.6,
61.26,
52.11,
58.17,
148.89,
155.73,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Analyze income city | {
"Age/Usia": [
70,
60,
54,
28,
39,
24,
54,
30,
30,
18,
53,
46,
34,
32,
40,
61,
34,
26,
67,
24,
18,
48,
49,
68,
48,
26,
39,
28,
50,
61,
44,
18,
54,
32,
70,
35,
23,
57... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Kelompokkan nilai pesanan rata-rata jenis kelamin | {
"Age/Usia": [
46,
39,
23,
18,
33,
41,
27,
66,
48,
69,
68,
68,
52,
37,
70,
46,
23,
58,
35,
21,
21,
25,
60,
25,
22,
23,
28,
69,
18,
28,
52,
67,
44,
28,
63,
32,
55,
67... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Compare market share time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Beverages_2', 'Fruits_3'], 'Sales/Penjualan': [941, 715, 129], 'Price/Harga': [26.36, 50.69, 172.84], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', ... |
Calculate average order value gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Snacks_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [287, 139, 621], 'Price/Harga': [140.03, 118.54, 34.64], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan',... |
Segment price time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Berlin",
"Sydney",
"London",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"Germany",
"Australia",
"UK",
"Braz... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Berlin', 'Sydney', 'London', 'São Paulo'], 'Country/Negara': ['Japan', 'Germany', 'Australia', 'UK', 'Brazil'], 'Region/Wilayah': ['Asia', 'Europe', 'Oceania', 'Eu... |
Visualize market share gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Singapore",
"Dubai",
"São Paulo",
"New York",
"Paris"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Singapore",
"UAE",
"Brazil",
"US",
"Fra... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Singapore', 'Dubai', 'São Paulo', 'New York', 'Paris'], 'Country/Negara': ['Singapore', 'UAE', 'Brazil', 'US', 'France'], 'Region/Wilayah': ['Asia', 'Middle East', 'South A... |
Analisis pengunjung negara | {
"Age/Usia": [
23,
26,
18,
66,
41,
31,
59,
40,
27,
37,
46,
26,
58,
37,
18,
70,
63,
29,
19,
33,
68,
56,
49,
59,
41,
53,
65,
21,
66,
61,
34,
54,
68,
64,
62,
68,
55,
57... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Calculate revenue gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [487, 734, 625], 'Price/Harga': [128.98, 155.41, 18.57], 'Category/Kategori': ['Automotive/Otomotif', 'Automo... |
Segment growth rate product | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Jakarta",
"New York",
"Paris",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"Indonesia",
"US",
"France",
"Japan"... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Jakarta', 'New York', 'Paris', 'Tokyo'], 'Country/Negara': ['India', 'Indonesia', 'US', 'France', 'Japan'], 'Region/Wilayah': ['Asia', 'Asia', 'North America', 'E... |
Segment average order value customer segment | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Beverages_2', 'Snacks_3'], 'Sales/Penjualan': [377, 949, 233], 'Price/Harga': [132.38, 179.32, 131.21], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan'... |
Korelasi penjualan and/dan diskon | {
"Age/Usia": [
35,
52,
50,
28,
45,
39,
39,
57,
64,
63,
23,
25,
50,
26,
37,
40,
35,
28,
35,
57,
70,
69,
35,
48,
32,
45,
46,
19,
60,
45,
50,
67,
62,
43,
20,
66,
64,
21... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Optimalkan diskon kota | {
"Age/Usia": [
34,
39,
68,
20,
44,
43,
65,
30,
46,
60,
42,
59,
22,
67,
21,
54,
53,
45,
55,
23,
40,
45,
27,
52,
55,
28,
25,
62,
22,
21,
54,
38,
31,
39,
30,
69,
25,
65... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Optimize growth rate region | {
"Age/Usia": [
20,
58,
22,
36,
23,
34,
60,
66,
49,
70,
70,
58,
31,
37,
53,
25,
19,
24,
59,
41,
64,
32,
57,
21,
50,
42,
48,
63,
40,
69,
37,
29,
62,
59,
21,
26,
64,
26... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Forecast visitors for/untuk quarter | {
"Age/Usia": [
55,
67,
49,
67,
70,
40,
46,
39,
57,
58,
52,
48,
47,
57,
29,
65,
66,
42,
67,
54,
23,
40,
54,
45,
52,
30,
19,
32,
26,
63,
55,
62,
40,
58,
44,
27,
25,
25... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Korelasi skor loyalitas and/dan nilai pesanan rata-rata | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Accessories_2', 'Tires_3'], 'Sales/Penjualan': [668, 1000, 174], 'Price/Harga': [156.75, 106.13, 72.02], 'Category/Kategori': ['Automotive/Otomotif', 'Automot... |
Analisis pangsa pasar segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mexico City",
"Singapore",
"Dubai",
"Tokyo",
"London"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Mexico",
"Singapore",
"UAE",
"Japan",
"... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mexico City', 'Singapore', 'Dubai', 'Tokyo', 'London'], 'Country/Negara': ['Mexico', 'Singapore', 'UAE', 'Japan', 'UK'], 'Region/Wilayah': ['North America', 'Asia', 'Middle... |
Identifikasi pendapatan negara | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['T-Shirt_1', 'Jeans_2', 'Shoes_3'], 'Sales/Penjualan': [693, 827, 495], 'Price/Harga': [194.49, 159.57, 38.84], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], ... |
Optimize conversion rate customer segment | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Sydney",
"Singapore",
"Dubai",
"New York",
"Mumbai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Australia",
"Singapore",
"UAE",
"US",
"In... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Sydney', 'Singapore', 'Dubai', 'New York', 'Mumbai'], 'Country/Negara': ['Australia', 'Singapore', 'UAE', 'US', 'India'], 'Region/Wilayah': ['Oceania', 'Asia', 'Middle East... |
Korelasi stok and/dan pangsa pasar | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"New York",
"Jakarta",
"Tokyo",
"Dubai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"US",
"Indonesia",
"Japan",
"UAE"
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'New York', 'Jakarta', 'Tokyo', 'Dubai'], 'Country/Negara': ['India', 'US', 'Indonesia', 'Japan', 'UAE'], 'Region/Wilayah': ['Asia', 'North America', 'Asia', 'Asia... |
Analyze visitors time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['TV_1', 'Headphones_2', 'Smartphone_3'], 'Sales/Penjualan': [778, 283, 759], 'Price/Harga': [20.08, 150.18, 126.77], 'Category/Kategori': ['Electronics/Elektronik', 'El... |
Analisis nilai pesanan rata-rata periode waktu | {
"Age/Usia": [
26,
68,
28,
21,
54,
53,
20,
32,
59,
24,
53,
43,
55,
66,
35,
39,
43,
33,
36,
68,
30,
20,
70,
41,
64,
38,
53,
28,
53,
21,
63,
59,
52,
59,
61,
58,
46,
51... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Visualisasikan pendapatan segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
112.78,
146.34,
114.85,
144.22,
161.49,
150.67,
101.69,
179.94,
77.52,
66.49,
150.08,
195.67,
91.44,
199.01,
100.35,
152.81,
57.23,
114.01,
160.31,
119.5,
80.17,
128.... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Korelasi penjualan and/dan pendapatan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [928, 390, 612], 'Price/Harga': [103.51, 165.5, 121.25], 'Category/Kategori': ['Electronics/Elektronik', 'Electr... |
Visualisasikan pengunjung kategori | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Dairy_2', 'Beverages_3'], 'Sales/Penjualan': [990, 959, 563], 'Price/Harga': [179.79, 38.51, 49.73], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', ... |
Korelasi pangsa pasar and/dan diskon | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
145.37,
81.41,
97.64,
96.1,
157.2,
168.41,
121.35,
149.41,
53.62,
146.14,
111.57,
134.7,
73.83,
68.37,
191.61,
166.85,
70.77,
189.06,
179.58,
71.58,
110.1,
195.89,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Calculate income gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [684, 578, 655], 'Price/Harga': [15.14, 173.53, 31.62], 'Category/Kategori': ['Electronics/Elektronik', 'Electro... |
Cluster loyalty score product | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
90.44,
129.78,
133.4,
69.49,
74.56,
152.69,
91.23,
192.64,
120.65,
169.61,
115.34,
105.25,
76.82,
51.93,
159.19,
60.39,
128.37,
132.5,
64.09,
94.58,
94.43,
54.32,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Correlate average order value and/dan price | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Snacks_2', 'Dairy_3'], 'Sales/Penjualan': [503, 130, 660], 'Price/Harga': [92.95, 46.68, 162.54], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Fo... |
Identify loyalty score gender | {
"Age/Usia": [
19,
66,
22,
33,
61,
58,
26,
27,
67,
35,
39,
37,
46,
39,
61,
63,
66,
24,
25,
43,
69,
24,
29,
30,
65,
70,
45,
21,
51,
70,
27,
50,
57,
36,
68,
24,
41,
19... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Correlate sales and/dan discount | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
141.77,
115.13,
78.61,
73.1,
107.99,
150.88,
190.91,
160.75,
55.41,
86.53,
69.24,
131.66,
163.5,
94.94,
107.2,
64.27,
53.4,
68.59,
113.06,
91.56,
179.34,
66.58,
1... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Analisis rasio konversi segmen pelanggan | {
"Age/Usia": [
51,
26,
18,
20,
53,
54,
35,
52,
27,
55,
31,
25,
41,
34,
46,
41,
54,
56,
66,
46,
36,
54,
56,
49,
50,
31,
57,
45,
43,
44,
32,
47,
27,
20,
65,
23,
54,
54... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Bandingkan pendapatan kategori | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Snacks_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [291, 161, 981], 'Price/Harga': [193.64, 30.01, 25.58], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', ... |
Analyze income gender | {
"Age/Usia": [
62,
56,
50,
63,
22,
36,
63,
45,
55,
66,
45,
59,
34,
58,
63,
61,
20,
33,
55,
57,
43,
52,
41,
56,
20,
53,
50,
31,
42,
60,
48,
57,
70,
51,
49,
47,
35,
56... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Correlate discount and/dan average order value | {
"Age/Usia": [
40,
67,
37,
60,
37,
31,
66,
33,
32,
46,
49,
29,
36,
25,
70,
43,
40,
23,
65,
63,
39,
66,
35,
63,
26,
43,
27,
21,
69,
44,
66,
29,
54,
44,
31,
37,
42,
60... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Korelasi pengunjung and/dan pendapatan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Sydney",
"Singapore",
"New York",
"Mumbai",
"Dubai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Australia",
"Singapore",
"US",
"India",
"... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Sydney', 'Singapore', 'New York', 'Mumbai', 'Dubai'], 'Country/Negara': ['Australia', 'Singapore', 'US', 'India', 'UAE'], 'Region/Wilayah': ['Oceania', 'Asia', 'North Ameri... |
Identify income gender | {
"Age/Usia": [
24,
38,
67,
52,
26,
67,
60,
55,
67,
40,
49,
25,
68,
66,
39,
52,
20,
32,
58,
49,
20,
41,
45,
23,
49,
22,
47,
42,
43,
58,
43,
34,
41,
33,
41,
51,
24,
21... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Identify loyalty score region | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Shoes_1', 'T-Shirt_2', 'Dress_3'], 'Sales/Penjualan': [931, 478, 389], 'Price/Harga': [94.79, 195.26, 39.68], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], '... |
Compare price country | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tools_2', 'Tires_3'], 'Sales/Penjualan': [486, 302, 181], 'Price/Harga': [40.78, 92.48, 66.94], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomo... |
Korelasi penjualan and/dan pendapatan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Jakarta",
"Paris",
"Dubai",
"Sydney",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Indonesia",
"France",
"UAE",
"Australia",
"U... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Jakarta', 'Paris', 'Dubai', 'Sydney', 'New York'], 'Country/Negara': ['Indonesia', 'France', 'UAE', 'Australia', 'US'], 'Region/Wilayah': ['Asia', 'Europe', 'Middle East', ... |
Optimize market share customer segment | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"São Paulo",
"Singapore",
"New York",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"Brazil",
"Singapore",
"US",
"... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'São Paulo', 'Singapore', 'New York', 'Sydney'], 'Country/Negara': ['Japan', 'Brazil', 'Singapore', 'US', 'Australia'], 'Region/Wilayah': ['Asia', 'South America', ... |
Optimalkan pendapatan kota | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
132.44,
70.91,
109.81,
74.49,
126.92,
171.3,
173.45,
198.28,
124.9,
160.92,
55.64,
132.64,
170,
193.39,
132.39,
115.33,
140.15,
162.13,
169.97,
166.54,
99.65,
127.21,... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Optimalkan stok kategori | {
"Age/Usia": [
51,
36,
20,
22,
24,
28,
41,
51,
26,
24,
47,
27,
45,
53,
58,
61,
52,
19,
55,
22,
59,
64,
43,
51,
52,
34,
20,
29,
25,
19,
45,
57,
49,
28,
44,
65,
48,
52... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Forecast price for/untuk week | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
92.23,
177.68,
136.39,
66.14,
104.57,
65.07,
156.06,
137.85,
134.79,
178.3,
60.41,
144.51,
60.44,
71.36,
82.55,
144.06,
91.44,
104.54,
135.04,
121.7,
94.92,
182.61,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Optimalkan harga wilayah | {
"Age/Usia": [
29,
36,
21,
52,
46,
50,
64,
18,
24,
60,
52,
48,
23,
26,
66,
22,
58,
40,
63,
35,
56,
59,
23,
65,
31,
49,
50,
20,
47,
24,
40,
22,
46,
29,
24,
41,
26,
58... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Forecast discount for/untuk month | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"São Paulo",
"Mumbai",
"Dubai",
"Sydney",
"Berlin"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Brazil",
"India",
"UAE",
"Australia",
"Germ... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['São Paulo', 'Mumbai', 'Dubai', 'Sydney', 'Berlin'], 'Country/Negara': ['Brazil', 'India', 'UAE', 'Australia', 'Germany'], 'Region/Wilayah': ['South America', 'Asia', 'Middl... |
Perkiraan pendapatan for/untuk tahun | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Dat... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [243, 438, 359], 'Price/Harga': [101.0, 166.71, 92.88], 'Category/Kategori': ['Electronics/Elektronik', 'Electro... |
Segment discount category | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['T-Shirt_1', 'Jeans_2', 'Shoes_3'], 'Sales/Penjualan': [829, 436, 853], 'Price/Harga': [71.49, 24.93, 134.44], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], '... |
Bandingkan pendapatan jenis kelamin | {
"Age/Usia": [
49,
51,
61,
57,
62,
69,
41,
45,
67,
32,
24,
51,
33,
66,
54,
18,
42,
70,
49,
39,
20,
25,
59,
19,
44,
35,
45,
63,
68,
59,
40,
57,
31,
44,
18,
45,
67,
38... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Hitung nilai pesanan rata-rata produk | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
100.05,
164.48,
107.65,
92.07,
55.82,
106.24,
190.07,
100.59,
154.71,
55.14,
60.74,
174.77,
192.34,
112.99,
181.82,
151.33,
114.45,
99.02,
131.52,
142.67,
100.67,
153... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Identify average order value product | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Oil_2', 'Tires_3'], 'Sales/Penjualan': [500, 633, 458], 'Price/Harga': [181.95, 32.51, 115.04], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Oto... |
Calculate stock gender | {
"Age/Usia": [
20,
55,
56,
47,
63,
37,
49,
28,
59,
64,
60,
23,
55,
36,
22,
20,
63,
52,
55,
54,
51,
18,
35,
44,
57,
31,
58,
59,
28,
19,
39,
56,
66,
20,
55,
69,
36,
38... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Compare visitors country | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [678, 225, 485], 'Price/Harga': [193.32, 32.54, 176.28], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan',... |
Segmentasi stok grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Dairy_2', 'Beverages_3'], 'Sales/Penjualan': [476, 253, 682], 'Price/Harga': [169.15, 156.44, 195.68], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan'... |
Optimize income customer segment | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
154.76,
50.67,
145.3,
83.4,
131.2,
99.75,
169.72,
122.08,
169.63,
132.9,
165.96,
111.98,
83.42,
185.62,
138.66,
158.74,
145.9,
152.57,
94.65,
67.98,
162.19,
141.95,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Kelompokkan skor loyalitas kota | {
"Age/Usia": [
23,
51,
70,
43,
33,
48,
58,
22,
40,
38,
23,
52,
52,
67,
63,
18,
62,
42,
18,
18,
57,
21,
45,
41,
43,
52,
46,
53,
69,
59,
58,
45,
56,
53,
59,
63,
23,
18... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'C... |
Bandingkan pendapatan grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"New York",
"Paris",
"Mexico City",
"Sydney",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"US",
"France",
"Mexico",
"Australia",
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['New York', 'Paris', 'Mexico City', 'Sydney', 'São Paulo'], 'Country/Negara': ['US', 'France', 'Mexico', 'Australia', 'Brazil'], 'Region/Wilayah': ['North America', 'Europe'... |
Analyze conversion rate city | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
146.18,
69.99,
136.88,
199.1,
73.31,
58.49,
123.39,
149.21,
91.01,
114.37,
153.42,
173.5,
65.95,
72.23,
199.07,
138.56,
50.34,
130.83,
71.59,
197.22,
116.69,
197.49,
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2... |
Visualize revenue gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Jakarta",
"Tokyo",
"Dubai",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"Indonesia",
"Japan",
"UAE",
"US"
... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Jakarta', 'Tokyo', 'Dubai', 'New York'], 'Country/Negara': ['India', 'Indonesia', 'Japan', 'UAE', 'US'], 'Region/Wilayah': ['Asia', 'Asia', 'Asia', 'Middle East',... |
Optimize price age group | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"São Paulo",
"New York",
"Sydney",
"Tokyo",
"Paris"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Brazil",
"US",
"Australia",
"Japan",
"Fran... | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['São Paulo', 'New York', 'Sydney', 'Tokyo', 'Paris'], 'Country/Negara': ['Brazil', 'US', 'Australia', 'Japan', 'France'], 'Region/Wilayah': ['South America', 'North America'... |
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