{"slug":"emircansoftware/MCP_DataScience","name":"MCP Server","description":"Automate data science stages using your own CSV data files.","category":"development","tags":[],"official":false,"stars":13,"transport":null,"install":[{"cmd":"pip install -r","imports":[]}],"tools":[{"name":"information_about_data","description":"Give detailed information about the data"},{"name":"reading_csv","description":"Read the csv file"},{"name":"visualize_correlation_num","description":"Visualize the correlation matrix for numerical columns"},{"name":"visualize_correlation_cat","description":"Visualize the correlation matrix for categorical columns"},{"name":"visualize_correlation_final","description":"Visualize the correlation matrix after preprocessing"},{"name":"visualize_outliers","description":"Visualize outliers in the data"},{"name":"visualize_outliers_final","description":"Visualize outliers after preprocessing"},{"name":"preprocessing_data","description":"Preprocess the data (remove outliers, fill nulls, etc.)"},{"name":"prepare_data","description":"Prepare the data for models (encoding, scaling, etc.)"},{"name":"models","description":"Select and evaluate models based on problem type"},{"name":"visualize_accuracy_matrix","description":"Visualize the confusion matrix for predictions"},{"name":"best_model_hyperparameter","description":"Tune the hyperparameters of the best model"},{"name":"test_external_data","description":"Test external data with the best model and return predictions"},{"name":"predict_value","description":"Predict the value of the target column for new input"},{"name":"feature_importance_analysis","description":"Analyze the feature importance of the data using XGBoost"}],"env_vars":[],"auth_type":"none","github":"https://github.com/emircansoftware/MCP_DataScience","homepage":"","server_url":"","status":"active","source":"mcpservers.org","updated_at":"Thu May 28"}