During my journey on DataCamp, I had the opportunity to work on several projects. Below are descriptions of four projects that I completed:
Mobile apps have become ubiquitous, and their creation can be both easy and profitable. In this project, I conducted a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. The goal was to gain insights from the data and devise strategies to drive growth and retention. The notebook above showcases the data analysis techniques used and the findings obtained.
With the vast number of movies and series available on the Netflix platform, it presented a perfect opportunity to apply data manipulation skills and delve into the entertainment industry. During this project, I focused on investigating movie lengths, as they appeared to be decreasing over time. To provide convincing results, I employed data visualization techniques using pandas and matplotlib. The project demonstrates the process of analyzing and visualizing Netflix movie data.
Commercial banks receive numerous credit card applications, many of which are rejected for various reasons such as high loan balances, low income levels, or excessive inquiries on credit reports. Manual analysis of these applications is tedious, prone to errors, and time-consuming. Machine learning offers a solution to automate this task, and most banks utilize it nowadays. In this notebook, I built an automatic credit card approval predictor using machine learning techniques, emulating the approach employed by real banks. The project involved exploratory data analysis, data preprocessing, model selection (logistic regression), and performance evaluation using accuracy and confusion matrix metrics. Additionally, I performed model tuning via grid search to enhance the model's predictive abilities.
By implementing a Logistic Regression Model and featuring high-traffic recipes on the home page, I successfully increased site subscriptions by 15% with an expected precision of 80%. This project demonstrates the application of logistic regression for improving site traffic and user engagement. The techniques used and the achieved results are showcased in the notebook.
These projects highlight my progress and skills acquired during my DataCamp journey. Each project includes detailed explanations, code, and visualizations to provide a comprehensive understanding of the data analysis process. Feel free to explore the notebooks for a deeper insight into my work.