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Udemy-Courses-Stas-2023-Prices-Data_analysis

visit the link to contribute to the analytics and edit it in kaggle : https://www.kaggle.com/code/krishna0000007/udemy-2

Udemy Course Data Analysis

Welcome to the Udemy Course Data Analysis repository! This project revolves around exploring and visualizing data from Udemy courses, analyzing their prices, ratings, and other attributes. By leveraging the power of Python and data visualization libraries, this project sheds light on valuable insights within the Udemy course dataset.

Udemy Courses

Table of Contents

Project Overview Dataset Description Data Analysis Exploratory Visualizations Usage Contributing Contact License Project Overview This project focuses on exploring and analyzing the Udemy course dataset, aiming to uncover trends, patterns, and insights related to course prices, ratings, and more. By utilizing Python for data analysis and visualization, the project provides a comprehensive understanding of the dataset's characteristics.

RESULTS of the EXPLORATORY DATA ANALYSIS AND LINEAR REGRESSION MODELS USED :

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Dataset Description

The dataset contains information about various Udemy courses, including details such as course title, price, number of subscribers, average rating, number of reviews, and more. The columns in the dataset include:

id title is_paid price headline num_subscribers avg_rating num_reviews num_comments num_lectures content_length_min published_time last_update_date category subcategory topic language course_url instructor_name instructor_url num_comments

Data Analysis

In this project, the dataset is analyzed to gain insights into various aspects of Udemy courses. This includes examining the distribution of prices, correlations between attributes, top categories, and more.

Exploratory Visualizations

The analysis is accompanied by visualizations that help convey the findings effectively. Exploratory graphs are used to illustrate trends, comparisons, and patterns within the dataset.

Price Distribution

Usage

Clone this repository to your local machine. Install the required Python libraries listed in requirements.txt. Run the Jupyter Notebook Udemy_Course_Analysis.ipynb to explore the data and visualize the insights. Contributing Contributions are welcome! If you have suggestions, improvements, or additional analyses, feel free to open an issue or a pull request.

Contact

For questions, suggestions, or collaborations, feedbacks pls feel free to contact at this mail - radheloyla@gmail.com

contacts :