/Website-Performance-Data-Analysis-Project

Briefly describe the objective of the project—e.g., analyzing website performance metrics over time, uncovering trends in user engagement, or evaluating channel-wise traffic quality.

Primary LanguageJupyter Notebook

🌐 Website Performance Data Analysis Project

Python
Jupyter
License

📑 Table of Contents

  1. 📌 Project Overview
  2. 📊 Dataset
  3. 🛠️ Technologies Used
  4. 🔍 Project Workflow
  5. 🚀 How to Run the Project
  6. 📈 Key Outcomes
  7. 🤝 Acknowledgments

📌 Project Overview

This project analyzes website performance metrics to uncover insights about user engagement, traffic behavior, and performance trends.

Using Python and visualization tools, the notebook provides:
✅ Traffic trend analysis
✅ Engagement behavior insights
✅ Channel performance comparisons
✅ Data-driven recommendations

📊 Dataset

  • Source: Website analytics export (Google Analytics or similar)
  • Main Features:
    • 📅 Date/Time of visit
    • 👥 Users & Sessions
    • ⏱️ Average Engagement Time
    • 🎯 Events per Session
    • 📊 Engagement / Bounce Rate
    • 🌍 Traffic Source (Organic, Paid, Referral, etc.)
  • Size: Multiple months of traffic data

🛠️ Technologies Used

  • Programming Language: Python 🐍
  • Environment: Jupyter Notebook 📓
  • Libraries:
    • pandas → Data cleaning & manipulation
    • numpy → Numerical computations
    • matplotlib & seaborn → Data visualization
    • plotly → Interactive plots

🔍 Project Workflow

  1. 📂 Data Loading & Cleaning

    • Import data, handle missing values, format columns

  2. 📊 Exploratory Data Analysis (EDA)

    • Summary statistics, detect traffic patterns & anomalies

  3. ⚙️ Feature Engineering

    • Derived metrics (e.g., session duration/user, engagement ratios)

  4. 📉 Data Visualization

    • Time-based trends, channel comparisons, engagement metrics

  5. 💡 Insights & Recommendations

    • Identify peak usage hours, best-performing channels, improvement strategies

🚀 How to Run the Project

  1. Clone the repository:
  git clone https://github.com/Prachi005748/Website-Performance-Data-Analysis-Project.git

2.Navigate into the folder:

cd Website-Performance-Data-Analysis-Project

3.Install dependencies:

pip install pandas numpy matplotlib seaborn plotly

4.Launch Jupyter Notebook:

jupyter notebook

5.Open and run:

Website performance analysis project.ipynb

📈 Key Outcomes

  • Identified traffic trends and engagement patterns
  • Highlighted high-performing vs. low-performing channels
  • Generated data-driven recommendations for website optimization

🤝 Acknowledgments

  • Dataset inspired by website analytics reports
  • Thanks to the Python Data Analysis Community 🙌 
    git clone https://github.com/Prachi005748/Website-Performance-Data-Analysis-Project.git

📬 Contact

If you have any questions, suggestions, or feedback, feel free to reach out: