Your task is to predict the scaled sound pressure level(dB) from aerodynamic and acoustic tests of two and three-dimensional airfoil blade section conducted in an anechoic wind tunnel.
This is a regression problem.
An airfoil is a shaped surface such as an airplane wind, tail, or propeller blade that produces lift and drag when moved through the air.
An anechoic wind tunnel is a type of wind tunnel that is designed to completely absorb sound waves.
The features convey the following information:
1. Frequency, in hertz.
2. Angle of attack(in degrees).
3. Chord length, in meters.
4. Free-stream velocity, in metres per second.
5. Suction side displacement thickness, in meters.
6. Scaled sound pressure level, in dB.
Plots must be implemented within 24 hours of being taken up.
Algorithms must be implemented within 2 days of being taken up.
Cross-validations/ensembles must be implemented within 3 days of being taken up.
The folder associated with the issue contains an IPython notebook containing a train-test split to get you started. It also contains the data set in CSV format.
Issue requirements / progress
All algorithms and ensembles must be scores using RMSE, Logloss and Accuracy metrics. Each pull request must only fulfill one of the tasks below.
Plots:
[ ] Box plot (2 vs 4)
[ ] Heatmap
[ ] Linear regression model
Algorithms:
[ ] Support vector machine
[ ] K-nearest neighbors
[ ] Random forest regressor
[ ] XGBoost
[ ] LightGBM
[ ] Multi-layer Perceptron
[ ] Decision Tree Regressor
Cross-validations/Ensembles:
[ ] 10-fold cross-validation of XGBoost
[ ] 10-fold cross-validation of LightGBM
[ ] Average (non-weighted) the predictions of a 10-fold cross-validation of XGBoost and LightGBM (both must be implemented in the fold)
Resources
List of resources that might be required / helpful.
Here are a few resources that may help you:
The following convention must be adhered to when placing your solution files.
Plots:
For Box-plot
/machineLearning/airfoil/plots/bp/<solution_file>
For Heatmap
/machineLearning/airfoil/plots/hm/<solution_file>
For Linear Regression Model
/machineLearning/airfoil/plots/lrm/<solution_file>
Algorithms:
For Support Vector Machine: /machineLearning/airfoil/algo/svm/<solution_file>
For K-Nearest Neighbors:
/machineLearning/airfoil/algo/knn/<solution_file>
For Decision Trees Regressor:
/machineLearning/airfoil/algo/dtm/<solution_file>
For Random Forest Regressor:
/machineLearning/airfoil/algo/rfm/<solution_file>
For Multi-layer Perceptron:
/machineLearning/airfoil/algo/mlp/<solution_file>
For XGBoost:
/machineLearning/airfoil/algo/xgb/<solution_file>
For LightGBM:
/machineLearning/airfoil/algo/lgbm/<solution_file>
Ensembles:
For 10-fold XGBoost: /machineLearning/airfoil/ens/10-xgb/<solution_file>
For 10-fold LightGBM:
/machineLearning/airfoil/ens/10-lgbm/<solution_file>
For average(non-weighted) of predictions of 10-fold XGBoost and 10-fold LightGBM
/machineLearning/airfoil/ens/avg/<solution_file>
Note
Please claim the issue first by commenting here before starting to work on it. Feel free to contact @amukh18 or @CinnamonRolls1 with any issues at any time.
Description
Your task is to predict the scaled sound pressure level(dB) from aerodynamic and acoustic tests of two and three-dimensional airfoil blade section conducted in an anechoic wind tunnel. This is a regression problem. An airfoil is a shaped surface such as an airplane wind, tail, or propeller blade that produces lift and drag when moved through the air. An anechoic wind tunnel is a type of wind tunnel that is designed to completely absorb sound waves.
The dataset can be found here.
The features convey the following information: 1. Frequency, in hertz. 2. Angle of attack(in degrees). 3. Chord length, in meters. 4. Free-stream velocity, in metres per second. 5. Suction side displacement thickness, in meters. 6. Scaled sound pressure level, in dB.
Details
The folder associated with the issue contains an IPython notebook containing a train-test split to get you started. It also contains the data set in CSV format.
Issue requirements / progress
All algorithms and ensembles must be scores using RMSE, Logloss and Accuracy metrics. Each pull request must only fulfill one of the tasks below.
Plots:
Algorithms:
Cross-validations/Ensembles:
Resources
List of resources that might be required / helpful. Here are a few resources that may help you:
Seaborn documentation a. Box plot: https://seaborn.pydata.org/generated/seaborn.boxplot.html b. Heat-map: http://seaborn.pydata.org/generated/seaborn.heatmap.html c. Linear regression model:https://seaborn.pydata.org/generated/seaborn.regplot.html
Directory Structure
The following convention must be adhered to when placing your solution files.
Plots:
/machineLearning/airfoil/plots/bp/<solution_file>
/machineLearning/airfoil/plots/hm/<solution_file>
/machineLearning/airfoil/plots/lrm/<solution_file>
Algorithms:
/machineLearning/airfoil/algo/svm/<solution_file>
/machineLearning/airfoil/algo/knn/<solution_file>
/machineLearning/airfoil/algo/dtm/<solution_file>
/machineLearning/airfoil/algo/rfm/<solution_file>
/machineLearning/airfoil/algo/mlp/<solution_file>
/machineLearning/airfoil/algo/xgb/<solution_file>
/machineLearning/airfoil/algo/lgbm/<solution_file>
Ensembles:
/machineLearning/airfoil/ens/10-xgb/<solution_file>
/machineLearning/airfoil/ens/10-lgbm/<solution_file>
/machineLearning/airfoil/ens/avg/<solution_file>
Note
Please claim the issue first by commenting here before starting to work on it. Feel free to contact @amukh18 or @CinnamonRolls1 with any issues at any time.