Mushroom Classification

This project focuses on classifying mushrooms as either poisonous or edible using machine learning techniques.

Dataset

The project uses the famous Mushroom Dataset, which contains descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family.

Contents

• Mushroom_Classification.ipynb: Jupyter Notebook containing the data analysis, preprocessing, model training, and evaluation.

Getting Started

1. Clone this repository
2. Install the required dependencies (list them here)
3. Open Mushroom_Classification.ipynb in Jupyter Notebook or JupyterLab

Features

• Data exploration and visualization
• Preprocessing of mushroom characteristics
• Implementation of machine learning models for classification
• Model evaluation and performance metrics

Detailed Steps in Mushroom_Classification.ipynb

1. Data Loading and Exploration:

   • Import necessary libraries (pandas, numpy, matplotlib, seaborn, sklearn)
   • Load the mushroom dataset from 'mushrooms.csv'
   • Display basic information about the dataset (shape, columns, data types)
   • Show the first few rows of the data
   • Generate summary statistics of the features

2. Data Preprocessing:

   • Check for missing values
   • Encode categorical variables using LabelEncoder or OneHotEncoder
   • Split the dataset into features (X) and target variable (y)
   • Perform train-test split to create training and testing sets

3. Exploratory Data Analysis:

   • Visualize the distribution of edible vs poisonous mushrooms
   • Create correlation heatmap to identify relationships between features
   • Generate bar plots or pie charts for categorical features
   • Analyze feature importance using techniques like mutual information or chi-squared test

4. Model Selection and Training:

   • Choose and implement multiple classification algorithms (e.g., Random Forest, Logistic Regression, SVM)
   • Train each model on the training data
   • Perform cross-validation to assess model performance

5. Model Evaluation:

   • Make predictions on the test set
   • Calculate and compare accuracy scores for each model
   • Generate confusion matrices to visualize true positives, false positives, etc.
   • Compute additional metrics like precision, recall, and F1-score
   • Plot ROC curves and calculate AUC scores

6. Feature Importance Analysis:

   • For tree-based models, extract and visualize feature importances
   • Identify the most influential characteristics for mushroom classification

7. Hyperparameter Tuning:

   • Perform grid search or random search for the best-performing model
   • Optimize hyperparameters to improve model performance

8. Final Model Selection and Evaluation:

   • Choose the best-performing model based on evaluation metrics
   • Retrain the final model on the entire dataset
   • Summarize the model's performance and key findings

9. Conclusion and Insights:

   • Summarize the most important features for mushroom classification
   • Discuss the model's strengths and limitations
   • Suggest potential applications of the model in real-world scenarios

Results

The results of the classification model, including accuracy and other relevant metrics, can be found in the Mushroom_Classification.ipynb notebook. For detailed findings and visualizations, please refer to the notebook.

Future Work

• Collect additional data to improve model robustness
• Experiment with ensemble methods or deep learning approaches
• Develop a web application for real-time mushroom classification
• Incorporate image recognition to classify mushrooms based on photographs

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, please contact Jacob Binu at [email protected]