Milk Grading

# Milk Grading (Classification)

### About dataset

This dataset is manually collected from observations. It helps us to build machine learning models to predict quality of milk.
This dataset consists of 7 independent variables ie pH, Temperature, Taste, Odor, Fat, Turbidity, Color.
Generally, Grade or Quality of the milk depends on these parameters. These parameters plays a vital role in predictive analysis of the milk.

### Usage.

Target variable is nothing but Grade of the milk . It can be
* 0 ( Bad )
* 0.5 ( Moderate)
* 1 ( Good )

if Taste , Odor, Fat, Turbidity are satisfies optimal conditions then they will assign 1 otherwise 0.
Temperature and ph are given their actual values in the dataset.

We have to perform data preprocessing, data augmentation techniques to build statistical and predictive models to predict the quality of the milk.

### Inspiration
To leverage the benefits of machine learning in the dairy industry.

### 关于数据集
本数据集系通过人工采集观察数据所得，旨在辅助构建机器学习模型以预测牛奶的品质。
数据集包含7个独立变量，即pH值、温度、口感、气味、脂肪、浑浊度和颜色。
通常，牛奶的等级或品质取决于这些参数，这些参数在牛奶预测分析中发挥着至关重要的作用。
### 使用说明
目标变量即牛奶的等级，可取以下值：
* 0（不良）
* 0.5（一般）
* 1（良好）
若口感、气味、脂肪和浑浊度满足最优条件，则赋予1，否则为0。
温度和pH值在数据集中以其实际数值呈现。
需进行数据预处理和数据增强技术，以构建统计和预测模型，预测牛奶的品质。
### 启发
本数据集旨在借助机器学习的优势，提升乳制品行业的效益。