The Power Market Ad Analysis project aimed to identify strategies to boost the acceptance rate for Power Market's advertising campaigns. The approach was to leverage consumer data and insights gathered from previous specialized ads, with the goal of increasing the success rate by at least 15% for the upcoming campaigns.

Various Python tools were used, including Matplotlib, Pypylot, Seaborn, and Statsmodels. Data cleaning and organization were carried out using Excel and Open Refine, which helped minimize outliers that deviated from the overall format of the data. The project involved performing a time series analysis on over a decade's worth of advertisement data from Power Market, which allowed us to build a comprehensive overview and forecast of the company's ad strategy.