Datasets:

kasunUdayanga
/

Tea_Yield_Prediction

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

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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 factors
feature-importance: Understanding which factors most influence tea production
outlier-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()

Downloads last month: 14

Size of downloaded dataset files:

2.84 MB

Size of the auto-converted Parquet files:

2.05 MB

Number of rows:

53,264