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1. 1. Model : SVM 2. EDA : Exploratory Data Analysis 3. Data Cleaning: Removing outliers 4. Data Transformation: StandardScaler and MinMaxScaler 5. Hyperparameter Tuning: GridSearchCV 6. Data Split: 70:30 7. 5 different sets of experiments with different combinations: 8. Results of each experiments
…tra code for other fixes
1. Changed helpers.py file 2. Updated python notebook to add the description of 70/30 ratio 3. Changed the function call train_svm in python notebook 4. Changed function call train_svm_with_hyperparameter_tuning
1. Changed helpers.py file 2. Updated python notebook to add the description of 70/30 ratio 3. Changed the function call train_svm in python notebook 4. Changed function call train_svm_with_hyperparameter_tuning
it can now be seen by double clicking the image
1. Fixed transformation code in helpers.py 2. Fixed comment in notebook "train-n-test-model-for-vehicle-recognition-from-silhouette-II"
* Add Logistic Regression Model for Winequality dataset Signed-off-by: SanchiMittal <[email protected]> * Add python modules Signed-off-by: SanchiMittal <[email protected]> * Minor Changes * Add Black Formatting
* initial contribution * initial updated
* KNN model trained and tested on generated.csv dataset * Effect of split ratio on performance * Hyperparameter tuning and cross validation implemented * Black formatting applied Co-authored-by: mlopatka <[email protected]>
* KNN model trained and tested on generated.csv dataset * Hyperparameter tuning and cross validation implemented * Relevance of a training datapoint to the performance of a trained model evaluated Co-authored-by: mlopatka <[email protected]>
* For #5: Calibration plot * reformatted calibration_plot using black * improve function code for better readability * renamed function calibration_plot to plot_calibration_curves, added explanatory text and curve interpretation in example notebook, reformatted notebook
…nce score (#67) * KNN model trained and tested on generated.csv dataset * Effect of split ratio on performance * Hyperparameter tuning and cross validation implemented * The effect of the number of folds on the cross_validated average performance score. The K-Nearest neighbor algorithm is used on the vehicles.csv dataset. * Black formatting applied * Update vary_folds.py Co-authored-by: mlopatka <[email protected]>
…fications Visualization for misclassifications
* visual for eeg * code restructured * #3 data-split space mapped * fixes issue3 * studied data splits for all classifiers * added graph in the loop * docstrings added * validation sets added * formatting * evaluated all classifiers * compared models * result added * calibration plot added * docstrings
Fixed #2, train and test a classification model on vehicles dataset
issue#2-trained-model-using-svm-for-vehicles-recognition-using-silhouette
* initial commit 1. Added notebook for issue # 4 Added helper function issue4_helper.py * updated code for cross_validation function * Added function to test cross validation score * Added new functions and fixed notebook 1. Added fix_outlier_with_boundary_value function 2. Added test_cross_validation function in helpers.py 3. Updated notebook with cross validation test using stratifiedkfold function 4. Updated comments in notebook * Updated notebook
* Data Loaded from vehicles.csv * Data visulaization and training model with ifferent algorithms * Evaluation of model is done. * Changed model from Logistic Regression to Support Vector Machine At first attempt i used three differnet models but Logistic Regression , Support Vector Machine and Decision Tree, and the overall accuracy with LR was better than any other but with changing validation parameters in SVM classification , model accuacy increased from 82% to 88%. * Delete train and test model-checkpoint.ipynb * Changed file named. * all python modules were added * docstrings were added * labels added in confusion matrix * Histogram colors were changed into single color * solved histogram issue * Update modules.py * changes made in histogram * Update modules.py * sorted histogram * Update modules.py * labels were added for confusion matrix * Python Custom Modules were added * Update Vehicle_Classifier.ipynb * Update modules.py * Update modules.py * Update modules.py * added labels in confusion matrix * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested Changes were made * Update modules.py * change categorical data into numerical data * change areguments in LR model * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Update modules.py * Requested changes were made * Update modules.py * Update modules.py * Update modules.py * Update Vehicle_Classifier.ipynb * updates file * Delete Untitled.ipynb * Update modules.py * code formatted using python Black * Requested changes were made * shifted classifier's code from modules.py to ModelEvaluation.py * removed learning curves from file * added function for model evaluation in Model Evaluation file * updated svm and lr * added comments * added doc strings * Update ModelEvaluation.py * Update ModelEvaluation.py * Update Vehicle_Classifier.ipynb * Update modules.py * added descriptions * added interpretations of visualization * creating another branch from master * solving branching issues * main visulaization file is added * Added module for visualization of missclassification * added docstring and reformatted to python black * added interpretation of misclassification * removed unnecessary comments Co-authored-by: mlopatka <[email protected]>
…n the train-test split ratio (#72) * Contribution to issue #2 * Eliminació de l'arxiu de prova * Eliminació de l'arxiu de prova * Fixed axes when not numbers and removed superfluous function. * Minor changes to modules in file data_exploration.py. Co-authored-by: mlopatka <[email protected]>
* 1. Simple scatter plot 2. Violin and Box plots * work on previous model * starting work with prev model * 1. Added a modeule calibration_plots_module for calibration plots. 2. Added functions for both individual classifier plotting and multiple classifiers plots. * Fixed axes and elaborated calibration plots * Added outputs * Revert "1. Simple scatter plot" This reverts commit cb26342. * Python black formatting for modules * Added titles and labels for axes for plots * Added relative paths for files * Minor changes * Relative path to dataset in repo * Delete redundant files
* Wine_quality dataset trained * Recommend feature removed * recommend feature dropped from the training dataset * Added additional information in Readme.md for contributors
…om/Sidrah-Madiha/PRESC into Sidrah-Madiha-Comparative_Models_vehicle_dataset
* First attempt on vehicle data with a random forest calssifier * minor changes * Comparative model evaluation for vehicle dataset * first attempt for implementing task 7 * fixes #8 * fixed all change requests * fixed relative path, improved visualisation * minor fix in plot * added absolute distance for comapring with senstivity calculaation
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