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... |
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