• # Random Forest Regression Pt. 4 Training using One Feature with Grid Search and Randomised Grid Search

This is Pt. 4 in the series covering Random Forest Regression to predict the price of RY stock. It follows from Pt. 3 on Feature Engineering. In this post, training is done using the estimators and tools provided by Scikit-Learn.

We begin with a discussion of the theoretical concepts needed to undergo this process. These include hyperparameters, Grid Search, and pickling. Then, the models are trained using one feature with both Grid Search and Randomised Grid Search. The hypothesis that Randomised Grid Search is generally the...

• # Random Forest Regression Pt. 1 Algorithms, Importing, Exploring and Preprocessing the Data

This is the first post in a series which considers Regression using Random Forests to predict the price of the stock of the Royal Bank of Canada (ticker RY). The full technology stack includes Python, Pandas, NumPy, Matplotlib/Plotly, and Scikit-Learn.

Firstly, we discuss the algorithms of Decision Trees and Random Forests. Next, the data is imported from Yahoo! Finance with demonstrations for local CSV files as well as sourcing via the pandas_datareader. Afterwards, preliminary explorations are done with Pandas and its DataFrame. Finally, the data is preprocessed in preparation for visualisation and modeling.

• # Univariate Linear Regression with AMZN and Scikit-Learn

In this post, we explore univariate Linear Regression with Amazon stock (AMZN ticker) data using the Python data science ecosystem. The libraries used include Pandas, NumPy, Matplotlib and Scikit-Learn.

We start with a brief introduction to univariate linear regression and how it works. The data is imported, explored, and preprocessed using Pandas and Matplotlib. The model is then fitted with the data using both a train/test split and cross-validation with Scikit-Learn. The results for both scenarios are then discussed and compared.

• # Forecasting Stock Prices and Generating Buy Sell Signals

This is the first project I did with the Python data science stack. It is in the form of a Jupyter Notebook hosted on GitHub which can be found here. It covers a range of concepts and techniques including tools, data sources, data exploration and visualization, handling missing data, domain specific considerations and modeling.