- 2_IDAO2020.ipynb
- Models :
- Neural Network output vector 3 x 1
- Note :
- delete sat_id
- use x_sim, y_sim, z_sim to predict x, y, z respectively as a vector
- Result
- has big number of loss qrround 45841290.3181 for traiming
- Models :
- 2a_IDAO2020.ipynb
- Models :
- Same as before but delete normalization
- Result
- has big number of loss qrround 45972115.7892 for traiming
- Models :
- 3_IDAO2020.ipynb
- Models :
- Using linear regression by sklearn
- predict label one by one (use x_sim, y_sim, z_sim to predict x, y, z one by one)
- Note :
- sat_id not used to predict the data
- Result
- prediction result was so close with the simulation result see graph
- SMAPE output were below : (smape from prediction to real should be smaller than simulation to real)
- Models :
- 3a_IDAO2020.ipynb
- 4a_IDAO2020.ipynb
- 5_IDAO2020.ipynb
- 5a_IDAO2020.ipynb<
- 6_IDAO2020.ipynb
- 7_IDAO2020..ipynb
- Models :
- Using Neural network to predict each label
- To choose the predictor by find correlation each label (used label that has high correlation altough it sampled) with the target using this dictonary :
mean to predict x will be used sat_id, x_sim, y_sim, and Vz_sim. To see the correlation number vam see in cek_correlation.ipynb{'x':['x_sim', 'z_sim', 'Vy_sim'], 'y': ['x_sim', 'y_sim', 'Vz_sim'], 'z': ['sat_id', 'x_sim', 'z_sim', 'Vy_sim', 'Vz_sim'], 'Vx': ['y_sim', 'Vx_sim', 'Vz_sim'], 'Vy': ['z_sim', 'Vx_sim', 'Vy_sim', 'Vz_sim'], 'Vz': ['y_sim', 'z_sim', 'Vy_sim', 'Vz_sim']}
- Models :
- cek_correlation.ipynb
- Desc:
- Finding correlation between target and predictor and to make sure the correlation is valid in each condition also tried to sampling the dataframe
- Desc:
- Has been tried using RandomForestRegressor and SVR from sklearn byt the result was so bad
TO DO :
-
Add detail each jupyter file in description - Tuning parameters
- Tunning number of epoch:
- for epoch : 50 improvement 21% 0.184 vs 0.143
- try for epoch 100 add callbacks just make it final :)
-
Get target 0.15 if possible(I got you)
- Create submission
- Score for tes.csv
- Save the model
- Doing Track 2