Search is not available for this dataset
Rainfall_mm
float64 1.5k
3.5k
⌀ | Avg_Temp_C
float64 18
28
| Soil_pH
float64 4.5
6
| Fertilizer_kg_per_hectare
float64 200
500
⌀ | Sunshine_hours
float64 4
8
⌀ | Altitude_m
float64 500
2k
| Age_of_tea_plant_years
float64 3
30
| Yield_kg_per_hectare
float64 300
7k
| Season_Condition
int64 0
2
|
|---|---|---|---|---|---|---|---|---|
2,748.36
| 19.02
| 5.64
| 415.43
| 6.63
| 971.9
| 19.34
| 3,312.33
| 0
|
2,430.87
| 25.17
| 5.54
| 370.09
| 5.73
| 757.23
| 7.98
| 3,481.89
| 1
|
2,823.84
| 25.17
| 5.53
| null | 6.1
| 1,103.03
| 24.88
| 3,341.15
| 0
|
null | 22.51
| 4.97
| 278.29
| 7.09
| 1,172.5
| 13.25
| 3,905.33
| 2
|
2,382.92
| 21.11
| 5.82
| 209.43
| 5.45
| 1,159.95
| 9.1
| 3,429.68
| 0
|
2,382.93
| 27.15
| 4.83
| 274.69
| 6.98
| 1,104.5
| 11.41
| 2,969.36
| 1
|
3,289.61
| 25.08
| 6
| 357.6
| 6.68
| 967.72
| 11.33
| 4,017.57
| 1
|
2,883.72
| 19.46
| 5.05
| 374
| 5.6
| 1,160.49
| 11.38
| 3,619.61
| 1
|
2,265.26
| 24.05
| 4.83
| 486.49
| 5.01
| 1,851.93
| 13.42
| 4,030.07
| 2
|
2,771.28
| 23.9
| 6
| 409.85
| 5.28
| 1,443.62
| 14.61
| 3,856.28
| 0
|
2,268.29
| 25.16
| 4.77
| 394.15
| 6.53
| 962.44
| 3
| 4,623.47
| 1
|
2,267.14
| 28
| 5.7
| 379
| 5.27
| 1,323.41
| 7.53
| 3,607.68
| 2
|
2,620.98
| 20.54
| 5.47
| 239.2
| 4.45
| 1,132.45
| 7.87
| 2,888.3
| 2
|
1,543.36
| 24.66
| 5.63
| 213.21
| 5.87
| 1,771.92
| 3
| 3,595.42
| 0
|
1,637.54
| 27.49
| 5.88
| 462.62
| 6.14
| 1,235.39
| 17.37
| 3,514.51
| 1
|
null | 28
| 5.7
| 278.98
| 6.41
| 989
| 7.67
| 3,320.17
| 1
|
1,993.58
| 23.54
| 4.54
| 458.75
| 4.63
| 1,045.07
| 13.04
| 3,655.32
| 2
|
2,657.12
| 19.08
| 5.47
| 276.89
| 5.84
| 1,222.71
| 17.27
| 3,673.61
| 1
|
2,045.99
| 18
| 5.46
| null | 5.73
| 1,102.1
| 13.78
| 3,067.71
| 0
|
1,793.85
| 25.48
| 5.69
| 380.24
| 6.59
| 1,298.27
| 9.99
| 3,936.31
| 1
|
3,232.82
| 24.03
| 5.3
| 463.21
| 6.13
| 1,101.67
| 13.9
| 4,247.71
| 2
|
2,387.11
| 23.38
| 4.89
| 200.92
| 5.02
| 1,692.4
| 13.59
| 3,055.64
| 0
|
2,533.76
| 23.85
| 4.85
| 420.75
| 5.57
| 1,310.48
| 12.28
| 3,889.98
| 0
|
null | 25.94
| 5.24
| 210.85
| 7.03
| 972.04
| 18.32
| 2,568.03
| 1
|
2,227.81
| 24.64
| 4.7
| 376.68
| 7.31
| 935.64
| 3.53
| 3,956.47
| 2
|
2,555.46
| 24.16
| 5.02
| 322.71
| null | 1,236.77
| 17.65
| 3,241.97
| 0
|
1,924.5
| 21.22
| 5.06
| 425.48
| 5.7
| 1,378.74
| 3
| 3,375.48
| 2
|
2,687.85
| 26.12
| 5.17
| 333.88
| 5.44
| 1,843.87
| 19.93
| 3,560.79
| 0
|
2,199.68
| 21.27
| 6
| 293.29
| 6.48
| 1,698.81
| 6.32
| 3,121.84
| 2
|
2,354.15
| 25.9
| 4.82
| 346.95
| null | 1,190.4
| 14.07
| 3,337.57
| 1
|
2,199.15
| 21.82
| 4.5
| null | 6.47
| 1,253.7
| 16.5
| 2,884.55
| 1
|
3,426.14
| 24.77
| 5.47
| 329.26
| 5.13
| 1,465.1
| 19.51
| 3,699.25
| 0
|
2,493.25
| 28
| 4.89
| 317.61
| 6.61
| 1,491.69
| 6.53
| 4,356.32
| 0
|
1,971.14
| 24.98
| 5.87
| 262.64
| 4.1
| 894.04
| 8.08
| 3,256.35
| 0
|
2,911.27
| 27.25
| 5.22
| 415.66
| 6.32
| 1,305.88
| 17.3
| 4,372.28
| 0
|
1,889.58
| 21.89
| 5.35
| 312.71
| 6.02
| 1,444.12
| 12
| 4,255.55
| 2
|
2,604.43
| 23.29
| 4.63
| 370.73
| 7.59
| 1,178.05
| 11.98
| 3,305.01
| 0
|
1,520.16
| 21.94
| 5.33
| 408.07
| 6.36
| 1,727.51
| 23.29
| 3,514
| 1
|
1,835.91
| 18.02
| 5.94
| 294.81
| 5.9
| 1,763.42
| 18.71
| 2,494.15
| 0
|
2,598.43
| 26.21
| 5.74
| 398.3
| 8
| 945.47
| 11.44
| 4,059.01
| 0
|
2,869.23
| 24.71
| 5.39
| 328.88
| 6.04
| 1,580.9
| 6.75
| 4,170.15
| 1
|
2,585.68
| 24.49
| 4.69
| 324.12
| 7.21
| 1,278.66
| 10.19
| 3,304.8
| 1
|
2,442.18
| 26.7
| 5.55
| 402.32
| 5.87
| 979.67
| 13.78
| 3,505.83
| 2
|
2,349.45
| 19.59
| 5.49
| 355.74
| 5.42
| 941.22
| 11.58
| 3,654.96
| 1
|
1,760.74
| 18.87
| 5.94
| 356.73
| 5.57
| 941.84
| 13.24
| 2,050.98
| 2
|
2,140.08
| 26.72
| 5.45
| 413.32
| 6.65
| 1,487.13
| 18.69
| 4,031.48
| 0
|
2,269.68
| 22.79
| 5.59
| 340.89
| 4.06
| 1,457.13
| 7.43
| 3,455.17
| 2
|
3,028.56
| 23.64
| 5.11
| null | 4
| 1,059.74
| 5.37
| 3,606.09
| 0
|
null | 18.6
| 5.02
| 306.8
| 5.83
| 1,344.86
| 7.4
| 4,241.82
| 1
|
1,618.48
| 27.42
| 5.54
| 383.16
| 6.51
| 1,343.33
| 21.82
| 3,113.76
| 0
|
2,662.04
| 25.01
| 5.43
| 350.42
| 5.23
| 1,173.36
| 19.02
| 3,598.21
| 0
|
2,307.46
| 21.27
| 5.44
| 392.98
| 5.15
| 1,306.25
| 13.93
| 3,974.26
| 1
|
2,161.54
| 28
| 5.37
| 217.49
| 5.94
| 1,040.2
| 3
| 3,332.81
| 0
|
2,805.84
| 18.91
| 4.5
| 385.75
| 5.76
| 1,119.7
| 15.29
| 2,585.82
| 2
|
3,015.5
| 22.01
| 5.6
| 386
| 6.75
| 1,067.8
| 8.82
| 2,720.45
| 2
|
2,965.64
| 25.96
| 5.64
| 391.44
| 5.11
| 965.35
| 6.55
| 3,783.76
| 1
|
2,080.39
| 26.46
| 5
| 404.77
| 5.6
| 1,369.53
| 10.15
| 3,431.14
| 1
|
2,345.39
| 18.52
| 5.64
| 309.9
| 5.92
| 1,964.62
| 10
| 4,184.83
| 1
|
2,665.63
| 22.15
| 5.22
| 308.22
| 6.3
| 1,768.29
| 9.82
| 3,845.32
| 1
|
2,987.77
| 25.24
| 5.36
| 285.34
| 6.62
| 1,347.14
| 16.39
| 3,406.32
| 0
|
2,260.41
| 27.42
| 4.96
| 263.16
| 5.34
| 1,080.59
| 19.93
| 3,113.7
| 1
|
2,407.17
| 27.17
| 5.29
| null | 6.24
| 1,345.93
| 10.33
| 3,221.65
| 1
|
1,946.83
| 25.1
| 4.5
| 350.93
| 6.84
| 815.27
| 13.41
| 3,611.9
| 2
|
1,901.9
| 23.27
| 5.15
| 460.83
| 5.21
| 1,928.66
| 18.63
| 3,426.05
| 2
|
2,906.26
| 22.46
| 6
| 311.87
| 7.67
| 969.88
| 19.58
| 4,103.88
| 0
|
3,178.12
| 24.49
| 5.38
| 284.25
| 6.9
| 1,032.31
| 6.49
| 4,048.54
| 0
|
2,463.99
| 27.3
| 5.14
| 274.07
| 7.65
| 1,182.18
| 17.17
| 3,852.11
| 1
|
3,001.77
| 22.36
| 5.12
| 500
| 5.46
| 1,170.18
| 9.08
| 3,605.38
| 0
|
2,680.82
| 22.18
| 4.96
| 207.94
| null | 1,462.1
| 8.68
| 3,194.91
| 1
|
2,177.44
| 26.44
| 5.36
| 381.47
| 7.46
| 1,088.48
| 6.8
| 3,303.17
| 2
|
2,680.7
| 26.18
| 4.8
| 313.76
| 7.78
| 772.57
| 5.98
| 3,400.73
| 1
|
3,269.02
| 21.5
| 5.68
| 200
| 6.62
| 1,023.09
| 16.69
| 3,853.45
| 1
|
2,482.09
| 19.04
| 5.73
| 279.74
| 6.28
| 654.71
| 7.99
| 3,805.09
| 1
|
3,282.32
| 18
| 5.65
| 286.42
| 5.98
| 1,024.32
| 9.85
| 3,419.64
| 0
|
1,500
| 23.39
| 5.79
| 325.13
| 4.7
| 1,783.32
| 12.44
| 3,126.26
| 0
|
2,910.95
| 19.69
| 5.77
| 350.06
| 4.86
| 1,059.89
| 5.37
| 3,930.02
| 2
|
null | 25.69
| 5.46
| 250.38
| 5.88
| 756.42
| 17.23
| 3,193.17
| 0
|
2,350.5
| 18
| 5.97
| 315.95
| null | 1,198.25
| 5.82
| 3,647.36
| 2
|
2,545.88
| 27.84
| 5.54
| 276.52
| 6.37
| 1,442.07
| 11.36
| 3,785.12
| 2
|
1,506.22
| 21.99
| 5.01
| 364.15
| 6.04
| 989.86
| 10.77
| 2,715.72
| 0
|
2,390.16
| 20.6
| 5.27
| 385.15
| 6.11
| 533.77
| 13.82
| 3,032.71
| 0
|
2,678.56
| 21.38
| 5.75
| 334.76
| 6.66
| 1,135.3
| 16.46
| 2,889.32
| 1
|
3,238.95
| 20.41
| 5.54
| 426.9
| 4.59
| 1,399.89
| 13.9
| 3,385.34
| 0
|
2,240.86
| 27.54
| 5.04
| 306.82
| 6.79
| 853.87
| 10.89
| 3,199.74
| 0
|
2,095.75
| 25.01
| 5.33
| 279.47
| 7.05
| 1,087.85
| 11.59
| 2,672.42
| 2
|
2,249.12
| 23.29
| 4.92
| 423.95
| 5.02
| 694.47
| 10.92
| 3,176.78
| 0
|
2,957.7
| 19.39
| 5.71
| 319.88
| 4.73
| 1,418.36
| 22.51
| 3,304.71
| 0
|
2,664.38
| 22.83
| 5.97
| 245.51
| 6.5
| 908.1
| 12.15
| 3,297.37
| 0
|
2,235.12
| 20.69
| 4.51
| 322.43
| 4.17
| 500
| 16.22
| 3,833.84
| 1
|
2,756.63
| 18
| 5.51
| 337.11
| 6.12
| 1,413.38
| 9.35
| 3,019.04
| 0
|
2,548.54
| 20.92
| 4.77
| 279.97
| 5.51
| 1,136.88
| 12.18
| 3,764.79
| 1
|
2,984.32
| 24.82
| 4.61
| 372.93
| 7.7
| 1,228.05
| 11.5
| 3,219.53
| 0
|
2,148.97
| 21.72
| 5.95
| 449.12
| 4
| 1,489.41
| 5.15
| 3,238.24
| 0
|
2,336.17
| 22.57
| 5.86
| null | null | 1,136.72
| 11.92
| 3,424.51
| 0
|
2,303.95
| 26.12
| 5.41
| 386.99
| 5.92
| 1,161.99
| 23.58
| 3,368.27
| 1
|
1,768.24
| 25.59
| 5.05
| 494.75
| 6.98
| 807.85
| 20.84
| 4,341.68
| 2
|
2,648.06
| 28
| 5.16
| null | 5.42
| 1,406
| 3.21
| 4,493.53
| 0
|
2,630.53
| 18
| 4.99
| 310.13
| 5.72
| 964.75
| 20.98
| 2,539.46
| 0
|
2,502.56
| 20.47
| 6
| 393.65
| 5.25
| 863.03
| 12.81
| 3,719.37
| 0
|
2,382.71
| 25.72
| 6
| 270.38
| 4.95
| 1,305.61
| 9.01
| 3,674.73
| 1
|
End of preview. Expand
in Data Studio
Sri Lanka Tea Yield Prediction Dataset
📋 Dataset Description
A synthetic dataset for predicting tea yield in Sri Lanka based on agricultural and environmental factors. This dataset simulates real-world conditions for machine learning regression tasks.
Dataset Summary
- Size: 53,264 samples × 10 features (including target)
- Type: Tabular/Structured data
- Task: Regression (predicting continuous tea yield)
- Domain: Agriculture, Climate, Food Production
Supported Tasks
tabular-regression: Predicting tea yield (kg/hectare) based on environmental and agricultural factorsfeature-importance: Understanding which factors most influence tea productionoutlier-detection: Identifying unusual yield patterns
Languages
English (feature names and descriptions)
📊 Dataset Structure
Data Fields
| Feature | Type | Range | Description |
|---|---|---|---|
| Rainfall_mm | float64 | 1500-3500 mm | Annual rainfall |
| Avg_Temp_C | float64 | 18-28°C | Average temperature |
| Soil_pH | float64 | 4.5-6.0 | Soil pH level |
| Fertilizer_kg_per_hectare | float64 | 200-500 kg/ha | Fertilizer usage |
| Sunshine_hours | float64 | 4-8 hours | Daily sunshine |
| Altitude_m | float64 | 500-2000 m | Elevation |
| Age_of_tea_plant_years | float64 | 3-30 years | Plant age |
| Yield_kg_per_hectare | float64 | 300-7000 kg/ha | Target variable |
| Season_Condition | int64 | 0,1,2 | Synthetic season indicator |
Data Splits
The dataset is provided as a single file suitable for train/validation/test splitting (recommended: 70/15/15).
🚀 Usage
Loading with Hugging Face Datasets
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("kasunUdayanga/Tea_Yield_Prediction")
# Convert to pandas DataFrame
df = dataset['train'].to_pandas()
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