This repository contains a collection of my personal Exploratory Data Analysis (EDA) projects. Each project involves exploring various datasets to gain insights, uncover patterns, and visualize trends.
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Description:This repository presents an exploratory analysis of the Amazon Prime Video dataset, focusing on both TV shows and movies available on the platform. The dataset comprises metadata encompassing crucial details such as titles, directors, cast members, release years, ratings, and durations of the shows and movies.The initial steps involve loading the dataset and augmenting it with additional features. Through this kernel, I delve into the dataset to extract meaningful insights and uncover notable findings. From identifying top-performing content to exploring trends in release years and genres, this analysis aims to provide a comprehensive understanding of the content landscape on Amazon Prime Video
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Key Findings: Here are the key findings from the analysis:
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Top 10 Movies: Identified the top 10 most popular movies based on various metrics such as ratings, viewer reviews, or duration.
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Language Distribution: Examined the distribution of languages across the available content, highlighting the prevalence of certain languages and their representation on the platform.
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Cumulative Distribution: Investigated the cumulative distribution of movies and TV shows based on release years, providing insights into the temporal evolution of content availability on Amazon Prime Video.
- Technologies Used: Numpys Pandas Matplotlib Seaborn
- Visualizations:
In today’s financial landscape, accurate credit score assessment is vital for both financial institutions and customers. Paisabazaar, a financial services company, helps individuals find and apply for banking and credit products. A key aspect of their business involves assessing customers’ creditworthiness to provide personalized financial recommendations and ensure sound risk management practices. This case study focuses on analyzing, classifying, and predicting credit scores based on a range of customer data, such as income, credit card usage, and payment behavior. The goal is to help Paisabazaar improve their credit assessment processes, reduce loan defaults, and optimize customer services through better-informed decisions.
In today’s fast-paced, globally connected market, FedEx Logistics plays a pivotal role in managing supply chains across various industries and regions. With the rapid growth of eCommerce and global distribution networks, maintaining efficient logistics operations is critical to staying competitive. This project focuses on analyzing data from FedEx Logistics to uncover insights that can optimize shipment processes, minimize costs, and enhance customer satisfaction. The dataset contains information on shipment modes, countries, vendors, line item values, weights, freight costs, delivery delays, and more, allowing for a comprehensive analysis of the company’s logistics performanctisfaction.
The hotel industry operates in a highly competitive environment, making it imperative for hoteliers to continuously adapt and innovate to remain ahead. A critical aspect of maintaining a competitive edge is understanding customer behavior and booking trends. This can be achieved through comprehensive Exploratory Data Analysis (EDA) of hotel booking data, which provides valuable insights into customer preferences, booking patterns, and potential areas for strategic improvement. This analysis aims to reveal key aspects of customer behavior and hotel booking trends, enabling hotels to enhance their business outcomes.
If you're interested in exploring any of the projects, simply click on the project link to navigate to the respective folder. Each project folder contains detailed documentation and code files.
Contributions are welcome! If you have any suggestions for improvements or would like to collaborate, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License.
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