This project explores machine learning and computer vision, focusing on the recognition of numbers in images. By employing feature extraction techniques such as Gray Scale conversion and Hu Moments, this project aims to capture crucial characteristics and patterns from the input images. The algorithm of choice for this task is the K-Nearest Neighbors (KNN), which enables accurate predictions by comparing the extracted features of the input image with known training examples.
- Dataset example:
- Accuracy: 0.947
| Nº | F1-Score |
|---|---|
| 0 | 0.968693 |
| 1 | 0.924536 |
| 2 | 0.953176 |
| 3 | 0.947212 |
| 4 | 0.933099 |
| 5 | 0.931304 |
| 6 | 0.971694 |
| 7 | 0.917526 |
| 8 | 0.896107 |
| 9 | 0.915320 |
The project focuses on estimating rainfall volume using radar data. The dataset, rainfall-volume-jan.csv, contains 107,136 rows of radar data captured every 15 minutes in January 2021. For this project the challenge was to deal with missing data in various ways.
- remove rows with missing data
- fill missing data with the mean of the column
- fill missing data with k-nearest neighbors
- fill missing data with the previous and next hours values
To predict the rainfall volume, the algorithm of choice was the Decision Tree Regressor and Support Vector Regressor (SVR).
The results are presented in the Jupyter Notebook rainfall.ipynb.
This project explores sentiment analysis, focusing on the classification of movie reviews as positive or negative. The datasets, reviews.txt.csv and imdb_csv.zip. To extract features from the text, the Bag of Words and Word2Vec techniques was used.
For the classification, the algorithm Sequence Models & Recurrent Neural Networks (RNNs) were used to create the models. In addition to that, a few pre-trained models from Hugging Face were used to compare the results.
Pre-trained models used:
- yosemite/autonlp-imdb-sentiment-analysis-english-470512388
- mrm8488/t5-base-finetuned-imdb-sentiment
- Loss: 0.2394
- Accuracy: 0.9071
