Rishabh-Gi-t/you_tube-trending-vids-analysis

This project aims to analyze and visualize YouTube data using Python's data analysis and visualization libraries. The data used in this project consists of YouTube video metadata, including information such as video titles, views, likes, and comments.

Requirements

• Python 3.x
• pandas
• numpy
• matplotlib
• seaborn Installation

1. Clone the repository to your local machine:

   git clone https://github.com/your_username/youtube-graph-analytics.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Ensure that you have the necessary dataset (USvideos.csv) in the project directory.
2. Open a Python environment or a Jupyter Notebook.
3. Import the required libraries:

   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt
   import seaborn as sns

4. Load the dataset:

   df = pd.read_csv("USvideos.csv")

5. Perform data analysis and visualization using the provided scripts or by writing your own code.

Features

• Data loading and preprocessing: The project includes functionality to load YouTube video metadata from a CSV file and preprocess it for analysis.
• Data visualization: Various plots and visualizations are generated to explore patterns and trends in the YouTube data, including histograms, scatter plots, and heatmaps.
• Correlation analysis: The project computes and visualizes the correlation matrix between different numerical features of the YouTube videos.

Contributing

Contributions to this project are welcome. If you have any ideas for new features, improvements, or bug fixes, feel free to open an issue or submit a pull request.