云鉴权>> wine-quality-classification>> 返回
项目作者： igerardoh

项目描述 ：
MLP classification model
高级语言： Jupyter Notebook
项目地址: git://github.com/igerardoh/wine-quality-classification.git
创建时间： 2020-04-22T14:39:02Z
项目社区：https://github.com/igerardoh/wine-quality-classification
开源协议：MIT License
下载


wine-quality-classification

Description

The dataset used on this classification model is the result of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. This dataset was taken from the following website: Wine recognition data

Number of Features

1) Alcohol
2) Malic acid
3) Ash
4) Alcalinity of ash
5) Magnesium
6) Total phenols
7) Flavanoids
8) Nonflavanoid phenols
9) Proanthocyanins
10) Color intensity
11) Hue
12) OD280/OD315 of diluted wines
13) Proline

Classes
  • class 1 : 59
  • class 2 : 71
  • class 3 : 48

Dependencies

You can use pip or conda to install the dependencies:

  • tensorflow
  • matplotlib
  • jupyter
  • pandas
  • seaborn
  • scikit-learn

Usage

If you want to try this program, download this repo and launch jupyter to run it on your machine.

- - - TODO - - -

  • ENVIRONMENT PREPARATION
    • Install library dependencies
    • Document installation and usage
  • DATA EXPLORATION
    • Add dataset description
    • Preview the structure of the dataset
    • Add data visualizations
  • DATA PREPROCESSING
    • Apply standarization to feature data
    • Apply one-hot encoding to categorical data
    • Split data into training and testing sets
    • Output preprocessed data for faster preloading
  • DATA ANALYSIS
    • Define network parameters
    • Define network structure
    • Add different network configurations
      • Define learning rate with different decaying methods
      • Set up cost, optimizer, and accuracy function with different configurations
    • Define model execution
    • Visualize evolution of MSE on training and testing datasets through epoch iteration
    • Visualize evolution of loss function
    • Visualize evolution of learning rate
    • Add log and summary writer
    • Add Tensorboard visualization
    • Add checkpoints for model restoration
  • MODEL DEPLOYMENT
    • Load a pretrained model
    • Test it with new data
  • OTHERS
    • Update README files
    • Update all nbviewer links
    • Add Tensorflow 1.x, Tensorflow 2.x, keras, tf.keras, and scikit-learn data analysis notebooks