{"id":19692298,"url":"https://github.com/shubhamdeepkeshav/project-ineuron-","last_synced_at":"2026-05-09T23:48:28.260Z","repository":{"id":249510187,"uuid":"811740736","full_name":"shubhamdeepkeshav/PROJECT-ineuron-","owner":"shubhamdeepkeshav","description":"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.","archived":false,"fork":false,"pushed_at":"2024-07-21T12:14:24.000Z","size":3154,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-10T08:58:32.135Z","etag":null,"topics":["data-preprocessing","data-science","feature-engineering","ineuron","machine-learning","model-evaluation","model-training","mushroomclassification","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🍄 Mushroom Classification with Machine Learning 🧠\nThis project explores the use of machine learning techniques to classify mushrooms as edible or poisonous based on their physical characteristics.\n\n#🛠️ Dependencies\nThis project requires the following Python libraries:\n\n    numpy 📊\n    \n    pandas 📅\n    \n    seaborn 🌈\n    \n    matplotlib.pyplot 📉\n    \n    warnings ⚠️\n    \n    scikit-learn (specifically linear_model, tree, svm, neighbors, naive_bayes, ensemble, decomposition, metrics) 🔍\n# 📥 Data Acquisition\nDownload the UCI Machine Learning Repository's Agaricus mushroom dataset (Link) 🍄.\nPlace the downloaded dataset (CSV file) in the same directory as this project 📁.\n# 🔍 Data Exploration and Pre-processing\n\n    Import necessary libraries 📚.\n\n    Load the mushroom dataset using pandas.read_csv() 📥.\n    \n    Explore basic information about the data using df.info(), df.describe(), and visualization techniques 🔎.\n    \n    Check for missing values using df.isnull().sum() ❓.\n    \n    Visualize the distribution of the target variable (class) using sns.countplot 📊.\n    \n    Understand the feature space by creating histograms, scatter plots, and other visualizations 📈.\n    \n    Address missing values using appropriate techniques like imputation or removal 🛠️.\n    \n    Encode categorical features into numerical representations 🔢.\n    \n    Use techniques like label encoding or one-hot encoding to transform categorical values into numerical features suitable for machine learning algorithms 🔠.\n    \n    Visualize the relationship between features using heatmaps with seaborn.heatmap() 🌡️.\n# 🧠 Model Training and Evaluation\n\n## Split Data 🧩\n\nDivide the dataset into training and testing sets using sklearn.model_selection.train_test_split 🔄.\nDimensionality Reduction (Optional) 🔬\n\nExplore dimensionality reduction techniques like Principal Component Analysis (PCA) from sklearn.decomposition to potentially improve model performance 📉.\nModel Selection and Training 🏋️\n\n# Train various machine learning models commonly used for classification tasks:\n    \n    Logistic Regression from sklearn.linear_model 📈\n    \n    Decision Tree from sklearn.tree 🌳\n    \n    Support Vector Machine (SVM) from sklearn.svm 🧩\n    \n    K-Nearest Neighbors (KNN) from sklearn.neighbors 👥\n    \n    Naive Bayes from sklearn.naive_bayes 🧠\n    \n    Random Forest from sklearn.ensemble 🌲\n    \n    Train each model using the training data 🏋️‍♂️.\n    \n    Model Evaluation 🏆\n\nEvaluate the performance of each model on the testing set using metrics like accuracy, precision, recall, and F1-score from sklearn.metrics 📏.\n\nVisualize the performance using techniques like classification reports and confusion matrices 🗂️.\n\n# Comparison and Selection ⚖️\n\nCompare the performance of different models based on the chosen evaluation metrics 📊.\n\nSelect the model that achieves the best performance on the testing set 🥇.\n\n# 📊 Visualization and Interpretation\n\nCreate visualizations (ROC curves) to compare the performance of different models using sklearn.metrics.roc_curve and sklearn.metrics.auc 📉.\nInterpret the results, providing insights into the most important features for classification based on feature importance scores from the chosen model 🔍.\n![mushroom](https://github.com/user-attachments/assets/683ef8fc-0c01-4d42-a66d-9b3a77276ca8)\n\n# 🗂️ Project Structure\n\n    mushroom-classification/\n    │\n    ├── data/\n    │   └── mushrooms.csv                # Dataset file 🍄\n    ├── notebook/\n    │   ├── 01_Mushroom1ml.ipynb         # Data exploration and visualization 📓\n    ├── models/\n    │   └── evaluation.py                # Script for evaluating models 🧮\n    ├── README.md                        # Project overview and instructions 📜\n    ├── requirements.txt                 # List of dependencies 📝\n    └── LICENSE                          # License for the project 📜\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamdeepkeshav%2Fproject-ineuron-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamdeepkeshav%2Fproject-ineuron-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamdeepkeshav%2Fproject-ineuron-/lists"}