This project presents a code/kernel used in a Kaggle competition promoted by Data Science Academy in September of 2019.
The goal of the competition was to create a Machine Learning model to help a robot to classify the floor surface on which it is using data collected by Inertial Measurement Units (IMU) sensors.
About the project: The data used in this competition was collected by the Tampere University Signal Processing Department in Finland. Data collection was performed with a small mobile robot equipped with IMU sensors on different floor surfaces at the university premises. The task is to predict which of the nine floor types (carpet, tiles, concrete, etc.) the robot is using sensor data such as acceleration and velocity. The success of this competition will help improve the navigation of autonomous robots on many different surfaces.
Competition page: https://www.kaggle.com/c/competicao-dsa-machine-learning-sep-2019
Files description:
- X_treino.csv - contains the training dataset with 487,680 rows and 13 columns.
- X_teste.csv - contains the test dataset with 488,448 rows and 13 columns.
- y_treino.csv - the surfaces for the training set.
- sample_submission.csv - a sample submission file in the correct format.
In this competition my best score was 0.622 and I got the position 26 on the leaderboard.