Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks.
- Install dependencies using requirements.txt: pip install -r requirements.txt
- Run notebooks as usual by using a Jupyter notebook server.
- Predicting Boston Housing Prices: A model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
- Supervised Learning: Finding Donors for CharityML: Testing out several different supervised learning algorithms to build a model that accurately predicts whether an individual makes more than $50,000, to identify likely donors for a fictional non-profit organisation.
- Unsupervised Learning: Creating Customer Segments: Analyzing a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for discovering internal structure, patterns and knowledge.
- Reinforcement Learning: Training a Smartcab to Drive: Creating an optimized Q-Learning driving agent that will navigate a Smartcab through its environment towards a goal.
Pandas, numpy, matplotlib, seaborn, sklearn, AWS, Heroku.