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[📌 Project Overview](#-project-overview)  \n2. [📊 Dataset](#-dataset)  \n3. [🛠️ Technologies Used](#️-technologies-used)  \n4. [🔍 Project Workflow](#-project-workflow)  \n5. [🚀 How to Run the Project](#-how-to-run-the-project)  \n6. [📈 Key Outcomes](#-key-outcomes)  \n7. [🤝 Acknowledgments](#-acknowledgments)  \n\n---\n\n## 📌 Project Overview  \nThis project analyzes **website performance metrics** to uncover insights about user engagement, traffic behavior, and performance trends.  \n\nUsing Python and visualization tools, the notebook provides:  \n✅ Traffic trend analysis  \n✅ Engagement behavior insights  \n✅ Channel performance comparisons  \n✅ Data-driven recommendations  \n\n---\n\n## 📊 Dataset  \n- **Source**: Website analytics export (Google Analytics or similar)  \n- **Main Features**:  \n  - 📅 Date/Time of visit  \n  - 👥 Users \u0026 Sessions  \n  - ⏱️ Average Engagement Time  \n  - 🎯 Events per Session  \n  - 📊 Engagement / Bounce Rate  \n  - 🌍 Traffic Source (Organic, Paid, Referral, etc.)  \n- **Size**: Multiple months of traffic data  \n\n---\n\n## 🛠️ Technologies Used  \n- **Programming Language**: Python 🐍  \n- **Environment**: Jupyter Notebook 📓  \n- **Libraries**:  \n  - `pandas` → Data cleaning \u0026 manipulation  \n  - `numpy` → Numerical computations  \n  - `matplotlib` \u0026 `seaborn` → Data visualization  \n  - `plotly` → Interactive plots  \n\n---\n\n## 🔍 Project Workflow  \n1. 📂 **Data Loading \u0026 Cleaning**  \n   - Import data, handle missing values, format columns  \n\n2. 📊 **Exploratory Data Analysis (EDA)**  \n   - Summary statistics, detect traffic patterns \u0026 anomalies  \n\n3. ⚙️ **Feature Engineering**  \n   - Derived metrics (e.g., session duration/user, engagement ratios)  \n\n4. 📉 **Data Visualization**  \n   - Time-based trends, channel comparisons, engagement metrics  \n\n5. 💡 **Insights \u0026 Recommendations**  \n   - Identify peak usage hours, best-performing channels, improvement strategies  \n\n---\n\n## 🚀 How to Run the Project  \n1. Clone the repository:\n ```bash\n   git clone https://github.com/Prachi005748/Website-Performance-Data-Analysis-Project.git\n   ```\n2.Navigate into the folder:\n```bash\ncd Website-Performance-Data-Analysis-Project\n```\n\n3.Install dependencies:\n```bash\npip install pandas numpy matplotlib seaborn plotly\n```\n\n4.Launch Jupyter Notebook:\n```bash\njupyter notebook\n```\n\n5.Open and run:\n```bash\nWebsite performance analysis project.ipynb\n```\n\n---\n\n## 📈 Key Outcomes\n-  Identified traffic trends and engagement patterns\n-  Highlighted high-performing vs. low-performing channels\n-  Generated data-driven recommendations for website optimization\n\n## 🤝 Acknowledgments\n- Dataset inspired by website analytics reports\n- Thanks to the Python Data Analysis Community 🙌 \n   ```bash\n   git clone https://github.com/Prachi005748/Website-Performance-Data-Analysis-Project.git\n   ```\n\n## 📬 Contact  \n\nIf you have any questions, suggestions, or feedback, feel free to reach out:  \n\n- *Name*: Prachi Paliwal\n- *Gmail*: prachipaliwal745@gmail.com \n- *GitHub*: [Prachi005748](https://github.com/Prachi005748)  \n- *LinkedIn*: [Prachi Paliwal](https://www.linkedin.com/in/prachi-paliwal-799126268/)  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprachi005748%2Fwebsite-performance-data-analysis-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprachi005748%2Fwebsite-performance-data-analysis-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprachi005748%2Fwebsite-performance-data-analysis-project/lists"}