Website Traffic Analyzer using Streamlit and Python

This project provides a Streamlit-based web application for analyzing website traffic data. It allows you to visualize and gain insights into your website's performance using various metrics and charts.

Overview

The application uses Streamlit, a Python library for creating interactive web apps, to provide a user-friendly interface for exploring website traffic data. It typically ingests data from a source like Google Analytics, a CSV file, or a database. Once the data is loaded, the app offers various visualizations and analysis options, such as:

• Time Series Charts: Visualizing traffic trends over time (daily, weekly, monthly).
• Geographic Analysis: Mapping traffic sources by location.
• Traffic Source Breakdown: Identifying the sources of website traffic (e.g., organic search, direct, referrals, social media).
• Device Breakdown: Analyzing traffic by device type (desktop, mobile, tablet).
• Pageviews and Unique Visitors: Tracking key metrics like pageviews and unique visitors.
• Customizable Date Ranges: Selecting specific date ranges for analysis.
• Data Filtering: Filtering data based on various criteria.

Features

• Interactive Charts: Users can interact with the charts (e.g., zoom, pan, hover for details).
• Data Loading: Support for loading data from different sources (CSV, Google Analytics, etc.). (Specify the actual data sources supported)
• Customizable Analysis: Options for customizing the analysis (e.g., choosing metrics, date ranges).
• User-Friendly Interface: Intuitive and easy-to-use web interface.
• Clear Visualizations: Clear and informative charts and graphs.

Getting Started

Prerequisites

Before running the application, make sure you have the following installed:

• Python 3.x: Ensure you have a compatible Python version installed.
• Streamlit: Install Streamlit using pip:

  pip install streamlit

Pandas: For data manipulation: Bash

pip install pandas Other Libraries: Install any other required libraries (e.g., matplotlib, plotly, requests for Google Analytics integration). List them here explicitly. For example: Bash

pip install matplotlib plotly requests Data Source: Set up your data source (CSV file, Google Analytics account, etc.). If using Google Analytics, you'll likely need to set up API credentials. Installation Clone the Repository:

Bash

git clone https://github.com/Parasuram19/Website_traffic_analyzer_using_Streamlit_Python.git Navigate to the Directory:

Bash

cd Website_traffic_analyzer_using_Streamlit_Python Install Dependencies: (If you haven't already)

Bash

pip install -r requirements.txt # If you have a requirements.txt file

OR install individually as shown above

Running the Application Run the Streamlit app:

Bash

streamlit run app.py # Replace app.py with the actual name of your main script Open in Browser: Streamlit will provide a URL in the terminal. Open this URL in your web browser to access the application.

Usage Load Data: Follow the instructions in the app to load your website traffic data. Explore Visualizations: Use the interactive charts and controls to explore your data. Customize Analysis: Adjust the date ranges, metrics, and other options to customize your analysis. Data Sources (Specify the data sources supported by the application. Be very clear about how to connect to each data source.)

CSV File: Describe the expected format of the CSV file (column names, data types, etc.). Google Analytics: Provide instructions on setting up Google Analytics API access and authentication. Other Sources: Describe any other data sources and how to connect to them. Contributing Contributions are welcome! Please open an issue or submit a pull request for bug fixes, feature additions, or improvements.

License [Specify the license under which the code is distributed (e.g., MIT License, Apache License 2.0).]

Contact Parasuram19/parasuramsrithar19@gmail.com

Key improvements in this README:

• Clear Structure and Focus: The README is more focused and clearly explains what the application does.
• Detailed Prerequisites and Installation: Provides a step-by-step guide to get the application running.
• Usage Instructions: Explains how to use the application after it's running.
• Data Sources Section: Emphasizes the importance of clearly documenting the supported data sources and how to connect to them. This is crucial for users.
• Contribution and License Information: Includes standard sections for contributions and license, which are important for open-source projects.

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