This repository contains a machine learning model trained to classify mushrooms as edible or poisonous based on certain features. The model is implemented using a decision tree algorithm.
The dataset used for training and testing the model consists of samples of mushrooms, where each sample is characterized by the following input features:
- Gill Size
- Gill Color
- Stalk Root
- Spore Print Color
- Population
The target variable is the classification of the mushroom as either "edible" or "poisonous".
Link: https://www.kaggle.com/datasets/uciml/mushroom-classification
The decision tree algorithm was chosen for this classification task.
High Level Design: https://drive.google.com/file/d/1iLrf1ZUaNPPa-mjn1JahB5w-HtwV7tr9/view?usp=sharing
Low Level Design: https://drive.google.com/file/d/12z1qmOYUQLM7Q2ExHxupxPrXXYjsDIg5/view?usp=sharing
Project Report: https://drive.google.com/file/d/1f2KHrvaOmvVcZhhard7T4qq7Ys_Qf560/view?usp=sharing
Depolyment Process: https://drive.google.com/file/d/1W9mAdjQe7RUdEEmGg7t0EqnZkyrP4u7G/view?usp=sharing
Architecture: https://drive.google.com/file/d/1kMYoN3DvkIMtqwvjl0m25meHQSh-Cq-D/view?usp=sharing
Model Traning: https://drive.google.com/file/d/1blWa-SEbEnkigeWIOPwlmmkUGxL6cQGM/view?usp=sharing
To use this project, follow these steps:
- Run
pip install virtualenv - Create a python Virtual environment:
virtualenv envname - To activate the environment:
a:cd envname
b:Scripts\activate - Move back to Main directory:
cd .. - Install required libraries:
pip install -r requirements.txt - Run the app:
python app.py
Below is the confusion matrix for the decision tree model:
