牧业牧场药品费用预测模型数据

可以用于牧场药品费用预测，输入为产奶量，出生率，牧场大小，动物数量，动物死亡率，其他费用，人工费用，设备维护费用，市场价格，水消耗量，饲料消耗，产肉量，总成本。输出为动物药品费用。该模型帮助解决了动物药品费用和牧场状况的关系建模的问题。通过调查采集牧场收支成本数据及牧场畜牧数据，并使用传统算法和多元线性回归算法预测牧场动物药品费用。该模型的输入变量包括产奶量，出生率，牧场大小，动物数量，动物死亡率，其他费用，人工费用，设备维护费用，市场价格，水消耗量，饲料消耗，产肉量，总成本。多元线性回归算法通过分析这些输入变量与动物药品费用之间的线性关系，确定每个变量的权重系数。在模型训练过程中，算法会利用历史数据进行优化，调整权重系数以最小化预测误差。模型通过最小二乘法等技术，根据输入的数据计算预测的动物药品费用，从而得出最终结果。通过这样的过程，模型能够将多个输入变量综合考虑，准确预测动物药品费用。

This dataset is designed for pasture veterinary drug cost prediction. Its inputs cover milk yield, birth rate, pasture size, animal population, animal mortality rate, other expenses, labor costs, equipment maintenance costs, market prices, water consumption, feed consumption, meat yield, and total cost, with the output being animal veterinary drug costs. This model addresses the problem of modeling the relationship between animal veterinary drug costs and pasture conditions. Data on pasture revenue, expenditure costs and livestock farming data were collected through surveys, and traditional algorithms and multiple linear regression algorithms were used to predict pasture animal veterinary drug costs. The input variables of the model include milk yield, birth rate, pasture size, animal population, animal mortality rate, other expenses, labor costs, equipment maintenance costs, market prices, water consumption, feed consumption, meat yield, and total cost. The multiple linear regression algorithm analyzes the linear relationship between these input variables and animal veterinary drug costs, and determines the weight coefficient of each variable. During model training, the algorithm utilizes historical data for optimization, adjusting the weight coefficients to minimize prediction errors. The model calculates the predicted animal veterinary drug cost based on the input data using techniques such as the least squares method to obtain the final result. Through this process, the model can comprehensively consider multiple input variables to accurately predict animal veterinary drug costs.

搜集汇总

数据集介绍

特点

该数据集名为“牧业牧场药品费用预测模型数据”，属于农、林、牧、渔业，数据来源于企业，规模为3674条，每年更新一次。数据集用于预测牧场药品费用，输入变量包括牧场大小、动物数量、出生率、死亡率等，输出为药品费用。通过多元线性回归算法进行预测，适用于牧场药品费用与牧场状况的关系建模。