Welcome to my data scientist portfolio, where I demonstrate my proficiency in applying advanced statistical and machine learning techniques to extract valuable insights from complex datasets. Through my work, I enable data-driven solutions that make a tangible impact. I am passionate about data science and its ability to provide meaningful solutions by analyzing and interpreting data. @ Nyokabi Waiganjo.
In this project, I present my work on Twitter sentiment analysis using Natural Language Processing (NLP). Sentiment analysis aims to determine the sentiment or emotion expressed in text data, in this case, tweets from Twitter. By applying NLP techniques and machine learning algorithms, I analyze and classify tweets to understand the sentiment behind them.
In this project, I focus on predicting customer churn using a machine learning approach. Customer churn refers to the phenomenon where customers discontinue using a product or service. By leveraging machine learning algorithms, I aim to build predictive models that can identify customers at risk of churning, enabling businesses to take proactive measures and retain their valuable customers.
This project aims to provide valuable insights for a real estate agency operating in King County, Washington, USA. Specifically, the agency seeks to provide accurate advice to homeowners on how home renovations can potentially increase the estimated value of their properties and homes, and by how much. This information will help the agency guide their clients towards making informed decisions on home renovations, which can maximize their return on investment when selling their properties.
This project analyzes Microsoft's foray into the film industry through the establishment of a film studio. We examine the motivations behind this venture, the opportunities and challenges involved, and the potential implications for the film industry. By evaluating the feasibility and potential impact of Microsoft's film studio, we provide valuable insights into this exciting development in the film industry..
The primary objective of this project is to accurately predict food production levels in Kenya. By analyzing historical data, current conditions, and relevant variables, the model aims to provide forecasts that reflect the expected output of crops, livestock, and other food sources.