CAMILO PORTFOLIO
Jr. Data Science Portfolio
DATA SCIENCE:
- Explored and analized a non-explicit dataset.
- Compared the accuracy of two classification models (KNN , Random Forest)
- Analized metrics to evaluate performance
- Optimized Random Forest training
- Data extraction and cleaning. In addition, creation of tables with relevant information, which were joined from the original tables of the cleaned database.
- Exploration of data in order to perform an analysis that details the information in each table. Visualizations were used to contemplate customer behavior from their data, helping me to draw conclusions and form profiles of each user according to their status.
- Training Machine Learning models to classify customers based on their status according to the economic loans received from the bank, and predict which customers will successfully complete their payments and which will become debtors.
The following document shows the data of a (seemingly) office supply company. An EDA was made in order to analyse the features of the dataset by using visualizations such as: histograms, time series, bar charts and tables. The modelling part includes a clustering model, using the “Elbow Method” and decomposing the components with the PCA functions.
NEURAL NETWORKS:
- Adaline Algorithm in it’s stochasthic, mini badges and badges form. This one is just an example of how Adaline works and 3 different ways to work with it.
- Adaline algorithm as an Audio Filter.
- Hardware input linear Regression:
- Hardware input filter prediction:
- And as a classification algorithm
- Image classification and recognition:
- Aproximation of functions:
- COVID Query Project
- Activies done during certification as Data Analyst