Datasets:
Simrita
/
FreshRetailNet-50K

Dataset card Data Studio Files
xet
Community
1
city_id
int64
store_id
int64
management_group_id
int64
first_category_id
int64
second_category_id
int64
third_category_id
int64
product_id
int64
dt
string
sale_amount
float64
hours_sale
list
stock_hour6_22_cnt
int32
hours_stock_status
list
discount
float64
holiday_flag
int32
activity_flag
int32
precpt
float64
avg_temperature
float64
avg_humidity
float64
avg_wind_level
float64
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2024-05-03
1.8
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2024-05-04
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2024-05-05
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2024-05-06
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2024-05-07
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2024-05-08
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2024-05-09
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2024-05-10
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2024-05-11
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2024-05-12
0.4
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2024-05-13
0.5
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2024-05-14
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2024-05-15
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2024-05-16
0.4
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2024-05-17
0.6
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2024-05-18
0.2
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2024-05-19
0.1
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2024-05-20
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2024-05-21
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2024-05-22
0.8
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2024-05-23
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2024-05-24
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2024-05-25
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2024-05-26
0.6
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2024-05-27
1
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2024-05-28
0.8
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2024-05-29
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2024-05-30
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2024-05-31
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2024-06-01
0.5
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2024-06-02
1
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2024-06-03
0
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2024-06-04
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2024-06-05
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2024-06-06
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2024-06-07
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2024-06-08
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2024-06-09
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2024-06-10
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2024-06-11
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2024-06-12
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2024-06-13
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886
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2024-06-14
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2024-06-15
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886
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2024-06-16
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886
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2024-06-17
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16
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0.666
0
1
10.8294
28.48
75.35
1.77
17
886
6
10
33
181
129
2024-06-18
1.5
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0
[ 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
0.666
0
1
15.8916
27.26
80.69
1.8
17
886
6
10
33
181
129
2024-06-19
0.1
[ 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
16
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
0.666
0
1
10.848
26.37
80.68
1.36
17
886
6
10
33
181
129
2024-06-20
0
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
16
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
0.666
0
1
14.4318
27.09
85.75
1.66
17
886
6
10
33
181
129
2024-06-21
0
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
16
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
0.666
0
1
10.8812
26.7
84.05
1.66
17
886
6
10
33
181
129
2024-06-22
0.8
[ 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0.1, 0.1, 0, 0, 0.3, 0, 0, 0.1, 0, 0, 0, 0, 0.1, 0, 0 ]
0
[ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
0.666
1
1
10.0754
26.23
83.63
1.36
17
886
6
10
33
181
129
2024-06-23
1.1
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0
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
0.666
1
1
10.3136
26.77
85.2
1.57
17
886
6
10
33
181
129
2024-06-24
0.5
[ 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
0
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
0.666
0
1
8.6652
26.57
81.87
1.57
17
886
6
10
33
181
129
2024-06-25
0.7
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5
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 ]
0.714
0
1
8.9143
27.29
84.91
2.02