Page View Time Series Visualizer

Overview

This project is part of the freeCodeCamp Data Analysis with Python Certification. The objective is to analyze and visualize time-series data from the freeCodeCamp forum using Python data visualization libraries.

The dataset contains the daily number of page views on the freeCodeCamp forum from May 2016 to December 2019. Through various visualizations, the project highlights trends, seasonal patterns, and growth in forum activity over time.

Features

• Imports and cleans time-series data using Pandas.
• Removes extreme outliers from the dataset.
• Creates a line chart to visualize daily page views over time.
• Generates a bar chart showing average monthly page views grouped by year.
• Produces box plots to analyze yearly trends and monthly seasonality.
• Saves all generated visualizations as image files.

Technologies Used

• Python
• Pandas
• Matplotlib
• Seaborn
• NumPy

Dataset

File: fcc-forum-pageviews.csv

The dataset contains:

• Date
• Daily Page Views

Data covers the period:

May 2016 – December 2019

Visualizations

1. Line Plot

Displays daily page views over time to identify overall growth trends.

Output: line_plot.png

2. Bar Plot

Shows average monthly page views grouped by year.

Output: bar_plot.png

3. Box Plots

Two box plots are generated:

• Year-wise Box Plot (Trend) – shows the distribution of page views across different years.
• Month-wise Box Plot (Seasonality) – shows seasonal patterns and monthly variations.

Output: box_plot.png

Project Structure

page-view-time-series-visualizer/
│
├── fcc-forum-pageviews.csv
├── time_series_visualizer.py
├── main.py
├── test_module.py
├── requirements.txt
├── README.md
├── line_plot.png
├── bar_plot.png
└── box_plot.png

Installation

Clone the repository:

git clone <your-repository-url>

Navigate to the project folder:

cd page-view-time-series-visualizer

Install dependencies:

pip install -r requirements.txt

Usage

Run the project:

python main.py

Generated files:

• line_plot.png
• bar_plot.png
• box_plot.png

Learning Outcomes

Through this project, I learned:

• Time-series data analysis
• Data cleaning and outlier removal
• Data aggregation using Pandas
• Building line charts, bar charts, and box plots
• Visualizing trends and seasonality in real-world datasets
• Creating professional data visualizations with Matplotlib and Seaborn

Skills Demonstrated

• Data Analysis
• Data Visualization
• Pandas
• Matplotlib
• Seaborn
• Python Programming
• Statistical Data Exploration

Acknowledgements

This project was completed as part of the freeCodeCamp Data Analysis with Python Certification.

Author

Bhavana R