Welcome to my first portfolio project! In this engaging project, I embarked on an exciting journey into the realm of movie data analysis. My focus was on the latest top 1000 movies listed on IMDB, aiming to extract valuable insights and trends from this rich dataset.
Project Overview
Data Collection: To gather the necessary data, I harnessed the power of web scraping, employing Python’s Request and BeautifulSoup4 libraries. This efficient approach allowed me to extract information from IMDB’s website, including details about the top 1000 movies.
Exploratory Data Analysis (EDA): Once the data was collected, I embarked on a comprehensive EDA journey. This involved a harmonious blend of both numeric and visualization techniques to unveil hidden insights and patterns within the movie dataset.
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Numeric EDA: For the numeric aspect of EDA, I harnessed the capabilities of Pandas, a robust data manipulation library. I performed various tasks, such as data cleaning, aggregation, and statistical analysis. This enabled me to gain a deeper understanding of the dataset’s structure, the distribution of values, and crucial statistical metrics.
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Visual EDA: To visually communicate complex findings, I turned to Seaborn, a popular data visualization library. I created a wide array of compelling charts, graphs, and plots, including histograms, scatter plots, box plots, and heatmaps. These visualizations were instrumental in unveiling trends, relationships, and outliers within the movie data.
Project Value
The culmination of this project serves as a valuable resource for movie enthusiasts, data enthusiasts, and anyone intrigued by the trends within the film industry. By exploring this project, you’ll gain insights into the latest top 1000 IMDB movies and the fascinating stories they tell.