Marcia Guedes Portfolio

Google Ads campaign analysis in python

1. Project Overview

  • This project involved the creation of a hypothetical yet coherent dataset comprising 500 Google Ads campaigns.
  • The dataset includes key metrics such as Campaign Name, Clicks, Impressions, CPC, CTR, Conversions, and Conversion Rate (CVR).

2. Data Analysis in Python:

  • Data Transformation and Cleaning: The dataset underwent a thorough cleaning process to:

    • Remove null values
    • Eliminate duplicates
    • Trim unnecessary spaces
    • Identify and remove outliers to enhance data integrity.
  • Statistical Analysis: Various statistical analyses were performed to gain insights into campaign performance. Key metrics were calculated to identify trends and anomalies within the data.

3. Data Visualization

  • A range of visualizations was created to illustrate findings, including:
    • Scatter Plots: To examine relationships between different campaign metrics.
    • Histograms: For understanding the distribution of key performance indicators.
    • Box Plots: To identify the spread and outliers in metrics.
    • Bar Graphs: To compare performance across different campaigns.
    • Line Graphs: To visualize the relationship between two variables, providing insights into their correlation.

4. Outcome

  • This analysis not only highlights the effectiveness of various campaigns but also serves as a foundational study for future marketing strategies.