https://github.com/shubhamdeepkeshav/project-ineuron-

Classify mushrooms as 🍄 edible or poisonous 🚫 using machine learning 🤖. This project includes data preprocessing, training various models (Logistic Regression, Decision Tree, SVM, etc.) 🏋️, and evaluating performance with metrics and visualizations 📊. Perfect for learning classification and model comparison.
https://github.com/shubhamdeepkeshav/project-ineuron-

data-preprocessing data-science feature-engineering ineuron machine-learning model-evaluation model-training mushroomclassification visualization

Last synced: about 2 months ago
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Classify mushrooms as 🍄 edible or poisonous 🚫 using machine learning 🤖. This project includes data preprocessing, training various models (Logistic Regression, Decision Tree, SVM, etc.) 🏋️, and evaluating performance with metrics and visualizations 📊. Perfect for learning classification and model comparison.

• Host: GitHub
• URL: https://github.com/shubhamdeepkeshav/project-ineuron-
• Owner: shubhamdeepkeshav
• Created: 2024-06-07T07:53:37.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-21T12:14:24.000Z (almost 2 years ago)
• Last Synced: 2025-01-10T08:58:32.135Z (over 1 year ago)
• Topics: data-preprocessing, data-science, feature-engineering, ineuron, machine-learning, model-evaluation, model-training, mushroomclassification, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 3.01 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🍄 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 🔍.

![mushroom](https://github.com/user-attachments/assets/683ef8fc-0c01-4d42-a66d-9b3a77276ca8)

# 🗂️ 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 📜