This project was conducted as part of the Ironhack Bootcamp. The goal is to explore and analyze data using Python, Pandas, Matplotlib, Seaborn, and to perform an A/B Testing experiment to assess the impact of a new user interface.
cleaned_datav3_new.ipynb: Jupyter Notebook containing data analysis and A/B Testing.Vanguard.twb: Tableau Dashboard for visualizing experiment results.data/: Folder containing datasets used for analysis.
- Python (Pandas, NumPy, Matplotlib, Seaborn, SciPy)
- Git & GitHub
- Tableau (for data visualization)
- Exploratory Data Analysis (EDA) : Cleaning and analyzing trends in the dataset.
- Defining Key Performance Indicators (KPIs) : Measuring completion rate, time spent, and error rate.
- A/B Testing : Comparing the performance between the old and new user interfaces.
- Visualizing Results : Using Tableau and Python graphs.
- The A/B test was conducted to measure the impact of a new user interface on user engagement.
- Key metrics analyzed: completion rate, time spent, and error rate.
- The new interface resulted in a 15% increase in completion rate, suggesting better user engagement.
- However, the time spent per user increased slightly, indicating potential usability issues.
- A hypothesis test was performed to validate the results.
- The p-value obtained was below 0.05, indicating that the observed improvements are statistically significant.
- The effect size suggests that the impact of the new interface is moderate but meaningful.
- The increase in completion rate is a positive sign, but further usability testing is recommended.
- Users might need more guidance on certain steps, given the slight increase in time spent.
- Based on the results, it is recommended to deploy the new interface with minor adjustments to improve efficiency.
- Clone this repository :
git clone https://github.com/tchagdj/Statistics-Project.git