[Outreachy applications] Startup task: Train and test a classification model #2
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Hey! |
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I'll use the generated.csv dataset. |
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@KaairaGupta yes! in fact everyone is encouraged to work on this issue first |
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Okay. I'll use winequality.csv dataset |
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Hello @dzeber! I want to work on defaults.csv. |
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Sounds good! To be clear, it's also fine to work on the same dataset as someone else, as there are only 5 datasets currently. The goal is just to collect a diversity of models and approaches across these datasets. |
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Hello @dzeber, I'd like to work on eeg.csv. |
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Hello @dzeber, I will work with the winequality.csv |
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Hello @dzeber, I will be working on the defaults.csv dataset. |
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Hello @dzeber, I'll work with the winequality.csv |
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Hello @dzeber, I will be working on vehicles.csv. |
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Hello @dzeber , I'd like to work on vehicle.csv . |
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I would like to work on eeg.csv dataset. |
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I have been working on the generated.cvs dataset with SVMs. |
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I would like to work on defaults.csv @dzeber |
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I would be working on vehicles.csv @dzeber are we required to push a PR for this issue? |
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Hi all! I will be working on the winequality.csv dataset. |
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I will be working with winequality.csv dataset. |
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@dzeber I will be working on the default.csv dataset. |
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Hi @dzeber , I will be working on the vehicles.csv dataset. |
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Hello @dzeber, How can we submit the task after completion? |
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When you are done with this task, please submit a PR following the guidelines listed in the README. |
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Hello everyone, I am working on the wine quality dataset. I didn't pick a model yet. |
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Hi @dzeber , I shall be working on the vehicles.csv dataset |
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Hello @dzeber, I have some question regarding training data-set. I am using Vehicles data-set, I load the dataset. But my question is that should we train it against some rows or against some columns? |
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Hi @dzeber , I will be working on wine quality dataset. |
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hello, i am beginning my learning and exploration by working on wine quality dataset. |
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Is there any one who could till me that how to identify the dependent and independent variable in a data-set? |
these committed changes fixes issue #3 of traversal space of train-test splits using KNN model.in #2 i have used decision tree and further recommended outlier detection algorithm for classification. so in this PR i have used KNN and compared results with previous classfication.this PR uses already defined modules in #2.
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i have done the modifications requested in the same PR #26. please review it. i m sorry for the delay because i was not well. |
* Create Readme.md * Create files for exploring issue #2 * Format using black * Remove notebook from master * Increase modularization * create file for issue 6 * remove file added by mistake * Create notebook for issue 6 * Re-upload to the right folder * Delete file from the incorrect folder
* Update .gitignore * Preliminary Analysis * Helper modules (Bar and Hist graph) * Rough KNN algorithm implemented * Delete libraries.py * KNN classifier refactored and polished Returns only variable of intests for use the metrics calculations. * refactored for performance just the required functions imported * draft mlp classifier implemented to be reviewed * ... * Threshold conversion logic implemented Since knn.predict calculates a probability, we implement a logic for binary classification * Prelimary cleaning and knn model classification implemented! * Adjusted plor error with title placement * ... * Files reformated with 'Black' * Logistic Regression classifier * Refactores modules to improve modularity * Implemented Log Reg * Deleted mpl module to focus on knn and log reg * Refactors gotignore to my personal folder * refactored for readability * Implementation to add counts and relative percentages on bars graph * Refactored name #2, Completed Prelimary Analysis and Interpreted Results * Update Issue #2 - Train and test a classification model (PRESC).ipynb * Files reformated with 'Black' * Display Error corrected * Interpreted choice of hyper-parameters * Refactored and Added Modules used for Issue 3 * Prelimanry Analysis - Traversal of the space of train_test splits * Issue#3 complete * Removed Issues #2 and #3 ipynb * Issue #4 - completed Issue #4 - Traversal of the space of cross-validation folds * Delete defaults_data.csv Removing duplication of the existing data set which can be loaded from the repos root directory. Co-authored-by: mlopatka <[email protected]>
#2 Dropped quality, shifted the logic to python file, shifted imports to the top, added confusion_matrix and classification_report
…el (Stochastic Gradient Descent) on winequality.csv (#58) * adds incomplete files * adds .ipynb, .py and updates environment.yml * Delete winequality.ipynb removing duplicate files * Delete winequality_modules.py removing duplicate files * Delete winequality.ipynb removing incomplete files * Delete winequality_modules.py removing incomplete files * adds .ipynb, .py and updates environment.yml * adds description and deatiled reasoning for the methods, models and parameters used * drops quality column * updates .py file * adds files in a new folder * updates .yml
* WIP: #2 on the dataset 'eeg.csv' WIP: #2 on the dataset 'eeg.csv' * Add files via upload * Delete WIP: #2 on the dataset 'eeg.csv' * Delete #2 Train and test a classification model, eeg.csv-checkpoint.ipynb * WIP: #2 on the dataset 'eeg.csv' * Delete #2 Train and test a classification model, eeg.csv-checkpoint.ipynb * WIP: #2 Train and test a classification model, eeg.csv dataset * Delete #2 Train and test a classification model, eeg.csv.ipynb * Create README * WIP: #2 Train and test a classification model, eeg.csv dataset * Delete README
For #2: on the dataset 'winequality.csv'
Exploration of the Vehicles dataset based on Startup task #2
* Create Readme.md * Create files for exploring issue #2 * Format using black * Remove notebook from master * Increase modularization * create file for issue 6 * remove file added by mistake * WIP: Importance score for datapoints * Re-upload to the correct directory * Delete file from wrong directory
* Update .gitignore * Preliminary Analysis * Helper modules (Bar and Hist graph) * Rough KNN algorithm implemented * Delete libraries.py * KNN classifier refactored and polished Returns only variable of intests for use the metrics calculations. * refactored for performance just the required functions imported * draft mlp classifier implemented to be reviewed * ... * Threshold conversion logic implemented Since knn.predict calculates a probability, we implement a logic for binary classification * Prelimary cleaning and knn model classification implemented! * Adjusted plor error with title placement * ... * Files reformated with 'Black' * Logistic Regression classifier * Refactores modules to improve modularity * Implemented Log Reg * Deleted mpl module to focus on knn and log reg * Refactors gotignore to my personal folder * refactored for readability * Implementation to add counts and relative percentages on bars graph * Refactored name #2, Completed Prelimary Analysis and Interpreted Results * Update Issue #2 - Train and test a classification model (PRESC).ipynb * Files reformated with 'Black' * Display Error corrected * Interpreted choice of hyper-parameters * Exported clean data to csv * Update Issue #2 - Train and test a classification model.ipynb * Update Issue #2 - Train and test a classification model.ipynb * Create Issue #2 - Train and test a classification model.ipynb * Issue#2 complete Interpretation of hyperparameters and html fil added. * Updates on Issue#2 Net attempting PCA and WoE, to invetigate models performances * Merge conflicts fixed, Update observations
* Create Readme.md * Create files for exploring issue #2 * Format using black * Remove notebook from master * Create file for cross-validation exploration * Resolve conflict and update * Attempt to resolve conflict
…cles dataset (#162) * Removed an imported package not used in the code * removed files not meant to be on the master branch * Combined _model() functions into a single function * Update files to adhere to black formatting * Updating files to pass black formatting * Presenting the results of evaluation in place of hard-coding * Deleted a module not in use
dzeber
changed the title
and others
This is a good way to get started with the environment and the problem domain. It will also provide the basis for a test case for future work. At a minimum, you should:
Feel free to include any additional steps you feel are relevant or you are interested in trying out, such as:
When you start work on this task, please post a comment here indicating which dataset and model you will be working with so that other contributors can avoid duplicating your work.
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