The Mushroom Classification Project aims to predict whether a mushroom is edible 🥗 or poisonous ☠️ based on various physical characteristics 🍃🍂. The dataset used for this project is sourced from Kaggle 📂 and contains attributes such as cap shape, gill color, odor, and habitat 🌳.
- ✅ Develop a classification model to distinguish between edible and poisonous mushrooms 🍽️🛑.
- 📊 Analyze feature importance to understand which characteristics are most influential 🔍.
- 🛠️ Compare multiple machine learning models to identify the best performer 🚀.
- Source: Kaggle 📁
- Features: 22 categorical attributes 🎭
- Target Variable: Edible (E) 🥦 vs. Poisonous (P) ☠️
- Sample Size: 8,124 instances 📊
- Data Preprocessing 🛠️
- Convert categorical features into numerical format 🔢.
- Handle missing values (if any) ❌.
- Perform exploratory data analysis (EDA) 📈.
- Model Selection & Training 🤖
- Compare algorithms such as Decision Trees 🌳, Random Forests 🌲, SVM ⚖️, and Neural Networks 🧠.
- Use cross-validation to optimize model performance 🏆.
- Evaluation Metrics 📊
- Accuracy ✅, Precision 🎯, Recall 🔄, F1-score 🏅
- Confusion Matrix analysis 🔵🔴
- Deployment (Optional) 🚀
- Convert the model into a web-based tool 🌍 for public use 🎮.
- 🎯 High classification accuracy in distinguishing between edible and poisonous mushrooms 🍄.
- 📈 Insights into which features contribute most to classification 🧐.
- 🛠️ A deployable model for practical use in mushroom identification 📲.
- Python 🐍 (Pandas, NumPy, Matplotlib, Seaborn) 📊
- Machine Learning Libraries 🤖 (Scikit-learn, TensorFlow, XGBoost) 🏆
- Jupyter Notebook 📓 for interactive coding and visualization 🌟
- 📌 Week 1: Data Collection & Cleaning 🗂️
- 📌 Week 2: EDA & Feature Engineering 🔬
- 📌 Week 3: Model Training & Optimization 🧠
- 📌 Week 4: Evaluation & Finalization ✅
- 📌 Week 5: Documentation & Deployment 🌐
- Robin tomar 👩💻 (Machine Learning Engineer ) 🚀
For questions or collaborations, reach out via 📧 *[email protected] ✉️
🎉 Happy Coding & Stay Curious! 🔬🍄🚀