This project focuses on analyzing website performance data using Python. The analysis helps identify trends, user behavior, and performance metrics that can improve decision-making and optimize website strategy.
The notebook (performance aly.ipynb) includes data exploration,
cleaning, visualization, and insights derived from the dataset
(data-export (1).csv).
performance aly.ipynb→ Jupyter Notebook with step-by-step analysis.\data-export (1).csv→ Website performance dataset (raw data).\README.md→ Documentation for the project.
To run this project, install the following dependencies:
pip install pandas numpy matplotlib seaborn jupyter(Optional for extended analysis/visuals)
pip install plotly scikit-learn-
Clone or download this repository.\
-
Open the notebook in Jupyter:
jupyter notebook performance\ aly.ipynb -
Run all cells to reproduce the analysis.
- Data Cleaning & Preparation\
- Exploratory Data Analysis (EDA)\
- Website Traffic & Performance Trends\
- Visualizations (line plots, bar charts, pie charts, etc.)\
- Insights on user behavior and performance metrics
- Total sessions and users over time\
- Bounce rate and engagement trends\
- Top traffic sources\
- Conversion-related patterns
- Automate data import from Google Analytics or web APIs\
- Build dashboards using Power BI, Tableau, or Plotly Dash\
- Apply machine learning for traffic prediction