https://github.com/prachi005748/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.
https://github.com/prachi005748/website-performance-data-analysis-project
data-analyst data-cleaning data-preprocessing data-visualization data-visualization-python exploratory-data-analysis jupyter-notebook matplotlib numpy pandas python seaborn storytelling
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/prachi005748/website-performance-data-analysis-project
- Owner: Prachi005748
- Created: 2025-09-07T17:42:48.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-09-07T18:03:44.000Z (4 months ago)
- Last Synced: 2025-09-07T20:20:04.646Z (4 months ago)
- Topics: data-analyst, data-cleaning, data-preprocessing, data-visualization, data-visualization-python, exploratory-data-analysis, jupyter-notebook, matplotlib, numpy, pandas, python, seaborn, storytelling
- Language: Jupyter Notebook
- Homepage:
- Size: 534 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌐 Website Performance Data Analysis Project
---
## 📑 Table of Contents
1. [📌 Project Overview](#-project-overview)
2. [📊 Dataset](#-dataset)
3. [🛠️ Technologies Used](#️-technologies-used)
4. [🔍 Project Workflow](#-project-workflow)
5. [🚀 How to Run the Project](#-how-to-run-the-project)
6. [📈 Key Outcomes](#-key-outcomes)
7. [🤝 Acknowledgments](#-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:
```bash
git clone https://github.com/Prachi005748/Website-Performance-Data-Analysis-Project.git
```
2.Navigate into the folder:
```bash
cd Website-Performance-Data-Analysis-Project
```
3.Install dependencies:
```bash
pip install pandas numpy matplotlib seaborn plotly
```
4.Launch Jupyter Notebook:
```bash
jupyter notebook
```
5.Open and run:
```bash
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 🙌
```bash
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:
- *Name*: Prachi Paliwal
- *Gmail*: prachipaliwal745@gmail.com
- *GitHub*: [Prachi005748](https://github.com/Prachi005748)
- *LinkedIn*: [Prachi Paliwal](https://www.linkedin.com/in/prachi-paliwal-799126268/)