/BinaryClassifier

This repo contains a general binary classifier.

Primary LanguagePython

Binary Classification Project

This project is focused on performing binary classification on a dataset that I got online. The images are of towels in different states and is separated into 2 classes valid and non-valid. The goal of the project is to create a model that can accurately predict the target variable (i.e., the binary classification label) based on the given features.

Getting Started

To get started with this project, you will need the following versions of differnt tools.

  1. conda create -n binary_classification python=3.8
  2. conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.6 -c pytorch -c nvidia

Once you have all the necessary resources, you can proceed with the following steps:

  1. Clone this repository to your local machine.
  2. Install any required dependencies using [insert instructions, e.g., pip].
  3. Run the preprocessing script to prepare the data for modeling.
  4. Train and test the classification model using [insert name of machine learning framework or library, e.g., Scikit-learn].
  5. Evaluate the model's performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC).
  6. Make any necessary modifications to the model (e.g., hyperparameter tuning, feature engineering) to improve its performance.

Directory Structure

The project directory has the following structure:

.
├── data
│   ├── raw
│   ├── interim
│   └── processed
├── models
│   ├── model.pkl
│   └── model_metrics.txt
├── notebooks
│   ├── 01-data-exploration.ipynb
│   ├── 02-preprocessing.ipynb
│   └── 03-modeling.ipynb
├── src
│   ├── preprocess.py
│   └── train_model.py
├── README.md
└── requirements.txt
  • data/raw: contains the raw data file(s) downloaded from the source.
  • data/interim: contains the intermediate preprocessed data files.
  • data/processed: contains the final processed data files.
  • models: contains the trained model and its corresponding evaluation metrics.
  • notebooks: contains the Jupyter notebooks used for data exploration, preprocessing, and modeling.
  • src: contains the Python scripts for preprocessing and training the model.
  • README.md: this file you are currently reading.
  • requirements.txt: contains a list of required Python packages.

Results

After running the model, the best performance metrics obtained are [insert the best metrics, e.g., accuracy of 0.85]. This suggests that the model is [insert interpretation of model performance, e.g., fairly accurate] at predicting the binary classification label.

Conclusion

This project demonstrates the process of performing binary classification on a given dataset using a machine learning model. By following the steps outlined in this README, you can train your own model on your own dataset and make predictions on new data.