Dry Bean Classification Analysis 🚀

This project is focused on the classification of different types of dry beans using a machine learning approach. The dataset consists of 16 feature columns and 1 target column "Class" with 7 unique classes. The project is implemented with several packages and tools to facilitate data preprocessing, model training, and evaluation.

Table of Contents

• Dataset
• Installation
• Usage
• Components
• Model Training
• Logging and Monitoring
• Contributing

Dataset

The dataset used in this project includes the following features:

• Area
• Perimeter
• MajorAxisLength
• MinorAxisLength
• AspectRatio
• Eccentricity
• ConvexArea
• EquivDiameter
• Extent
• Solidity
• Roundness
• Compactness
• ShapeFactor1
• ShapeFactor2
• ShapeFactor3
• ShapeFactor4
• Class {
  • Seker
  • Barbunya
  • Bombay
  • Cali
  • Horoz
  • Sira
  • Dermason }

The target column is Class and it has 7 unique classes.

Installation & Requirements

To run this project, you need to install the following packages:

• mlflow
• jupyter
• scikit-learn
• pandas
• seaborn
• ucimlrepo
• xgboost
• imbalanced-learn
• Flask
• tqdm
• pydantic
• pylint
• matplotlib

Setting up

Clone the repository

git clone https://github.com/Jaykold/dry-bean-predictor.git

Navigate to the project directory

cd dry-bean-predictor

Create a virtual environment

You can install the required packages using the providedconda_dependencies.yml file:

conda env create -f conda_dependencies.yml

After creating your virtual environment, you can activate it using:

conda activate myenv // That's is the name specified in the .yml file

Or using requirements.txt:

pip install -r requirements.txt

Run pip install -e . to install the project in editable mode.

Run Jupyter Notebook

jupyter notebook

Open dry_bean.ipynb and execute the cells to preprocess the data and train the model

Components

Data Ingestion

The data_ingestion.py script is responsible for loading the dataset and performing initial data checks.

Data Preprocessing

The data_preprocess.py script handles data cleaning, feature engineering, and splitting the data into training and testing sets.

Model Training

The model_trainer.py script trains a classification model using the preprocessed data and evaluates its performance.

Pipeline

The pipeline directory contains script for model prediction.

• predict.py

Model training

To train the model, run the model_trainer.py script:

python src/components/model_trainer.py

This will train the model and save the results to the artifacts directory.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.