European Conference of Machine Learning - Principles and Practice of Knowledge Discovery in Databases conducted a Machine Learning competition where the task was to classify the land cover.

**Note**: Unfortunately, I was unable to submit my prediction data points on time. But I got 96.34 % accuracy which would have been **4th position** in the competition. Anyways I described my approach below.

Screenshot which shows the discussion over email:

The classification of land cover was divided into the following multi-class(9 classes) distribution.

Below picture depicts the class distribution.

## My Approach:

There were 230 columns which contained -ve & +ve data points representing the land cover.

### Outliers Removal:

All the columns had few rows with outliers which were removed. Boxplot depicting outliers in variable col1:### Feature Engineering:

I extracted several features out of which I ended up using the following features after feature selection. - coord1_col_1_std - Standard deviation of col1 grouped by coord1. - coord_diff_1 - coord1 minus coord2 variables. - coord_diff_2 - coord2 minus coord1 variables. - coords_combined - coord1 + coord2 variables. Overall, I ended up using 13 features after feature selection.### BoxCox Transformation for Skewed Variables :

Most of the variables were highly skewed.

I applied box-cox transformation on variables with (+-)ve 0.25 skew.

### Standardize data:

I applied Standard Scaling transformation to standardize the data.### Things that I tried which didn't improve Validation score:

- Polynomial features/ Feature interactions. - Mean, standard deviations, medians(Measures of central tendency) grouped by coordinates. - Robust Scaling before removing the outliers. - Stacking multiple models. - Max voting based on multiple models. - Dimensionality reduction.### Model Scores:

I tried several models which resulted in the following **local** validation scores:

Model | Validation Score |
---|---|

XGboost(Boosting): | 0.96 |

Linear Regression: | 0.68 |

Passive Aggressive Classifier: | 0.47 |

SGD Classifier: | 0.61 |

Linear Discriminant Analysis: | 0.67 |

KNeighbors Classifier: | 0.88 |

Decision Tree Classifier: | 0.89 |

GaussianNB: | 0.64 |

BernoulliNB: | 0.57 |

AdaBoost Classifier: | 0.50 |

Gradient Boosting Classifier: | 0.89 |

Random Forest Classifier: | 0.93 |

Extra Trees Classifier: | 0.95 |

Code available at github