This project involves the classification of mushrooms based on their features to determine whether they are edible or poisonous. It is a binary classification problem, where the target class is defined as 0 for edible mushrooms and 1 for those that are not edible or poisonous. The dataset, containing 54035 records and 9 distinct features, was sourced from Kaggle.
Objectives:
- Develop a model to accurately classify mushrooms as edible or poisonous.
- Compare the performance of various machine learning models.
Features Used: The dataset consists of 9 columns representing different characteristics of the mushrooms, such as cap shape, cap color, gill size, etc.
Machine Learning Models Implemented:
- K-Nearest Neighbors (KNN)
- Decision Tree
- Logistic Regression
- Support Vector Machine (SVM)
Libraries Utilized:
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Sklearn
Approach: Data preprocessing and cleaning. Exploratory data analysis using visualization tools like Matplotlib and Seaborn. Implementation and evaluation of various classification models. Comparison of model performance using metrics such as accuracy, precision, and recall.
Findings: The best-performing model was K-Nearest Neighbors (KNN), which achieved an accuracy of 98.78%. The project provides insights into the predictive capabilities of different models based on mushroom features.
This project offers a comprehensive analysis and implementation of machine learning techniques for binary classification, showcasing the strengths and limitations of various models in classifying mushrooms as edible or poisonous.