Welcome to my Data Science Portfolio on GitHub! This repository contains a variety of projects where I apply machine learning algorithms to solve real-world problems. Each project folder includes relevant datasets in CSV format, R or Python scripts detailing the analysis, and a README explaining the project's context and findings.
- R, Python
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- XGBoost
- Neural Networks
- Packages for modelling: tree, rpart, randomForest, xgboost, nnet, neuralnet, tseries, forecast, scikit-learn, keras, tensoflow
- Packages for visualization: ggplot2, matplotlib, seaborn
Feel free to explore the projects and reach out if you have questions or would like to collaborate!
Enjoy exploring my projects, and I appreciate any feedback or contributions to improve my work!
Note : I have often used a function createDummies, given by our instructor during the course, to create dummies out of character columns. This function can take a threshold frequncy value below which an observation won't get it's own dummy. I usually keep the threshold as 2% of total number of rows. You can find it's R script above. Another package fastDummies available on CRAN works great too but does't take threshold frequency as an input, as a result you might end up with loads of new dummies corresponding to observations that rarely occur in a column.