🍄 Mushroom Classification with Machine Learning 🧠

This project explores the use of machine learning techniques to classify mushrooms as edible or poisonous based on their physical characteristics.

#🛠️ Dependencies This project requires the following Python libraries:

numpy 📊

pandas 📅

seaborn 🌈

matplotlib.pyplot 📉

warnings ⚠️

scikit-learn (specifically linear_model, tree, svm, neighbors, naive_bayes, ensemble, decomposition, metrics) 🔍

📥 Data Acquisition

Download the UCI Machine Learning Repository's Agaricus mushroom dataset (Link) 🍄. Place the downloaded dataset (CSV file) in the same directory as this project 📁.

🔍 Data Exploration and Pre-processing

Import necessary libraries 📚.

Load the mushroom dataset using pandas.read_csv() 📥.

Explore basic information about the data using df.info(), df.describe(), and visualization techniques 🔎.

Check for missing values using df.isnull().sum() ❓.

Visualize the distribution of the target variable (class) using sns.countplot 📊.

Understand the feature space by creating histograms, scatter plots, and other visualizations 📈.

Address missing values using appropriate techniques like imputation or removal 🛠️.

Encode categorical features into numerical representations 🔢.

Use techniques like label encoding or one-hot encoding to transform categorical values into numerical features suitable for machine learning algorithms 🔠.

Visualize the relationship between features using heatmaps with seaborn.heatmap() 🌡️.

🧠 Model Training and Evaluation

Split Data 🧩

Divide the dataset into training and testing sets using sklearn.model_selection.train_test_split 🔄. Dimensionality Reduction (Optional) 🔬

Explore dimensionality reduction techniques like Principal Component Analysis (PCA) from sklearn.decomposition to potentially improve model performance 📉. Model Selection and Training 🏋️

Train various machine learning models commonly used for classification tasks:

Logistic Regression from sklearn.linear_model 📈

Decision Tree from sklearn.tree 🌳

Support Vector Machine (SVM) from sklearn.svm 🧩

K-Nearest Neighbors (KNN) from sklearn.neighbors 👥

Naive Bayes from sklearn.naive_bayes 🧠

Random Forest from sklearn.ensemble 🌲

Train each model using the training data 🏋️‍♂️.

Model Evaluation 🏆

Evaluate the performance of each model on the testing set using metrics like accuracy, precision, recall, and F1-score from sklearn.metrics 📏.

Visualize the performance using techniques like classification reports and confusion matrices 🗂️.

Comparison and Selection ⚖️

Compare the performance of different models based on the chosen evaluation metrics 📊.

Select the model that achieves the best performance on the testing set 🥇.

📊 Visualization and Interpretation

Create visualizations (ROC curves) to compare the performance of different models using sklearn.metrics.roc_curve and sklearn.metrics.auc 📉. Interpret the results, providing insights into the most important features for classification based on feature importance scores from the chosen model 🔍.

🗂️ Project Structure

mushroom-classification/
│
├── data/
│   └── mushrooms.csv                # Dataset file 🍄
├── notebook/
│   ├── 01_Mushroom1ml.ipynb         # Data exploration and visualization 📓
├── models/
│   └── evaluation.py                # Script for evaluating models 🧮
├── README.md                        # Project overview and instructions 📜
├── requirements.txt                 # List of dependencies 📝
└── LICENSE                          # License for the project 📜