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https://github.com/srinivas39322/analytical_insights_into_ios_app_profitability

EDA and Analytical Insights and Predictions on Apple App Store.
https://github.com/srinivas39322/analytical_insights_into_ios_app_profitability

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EDA and Analytical Insights and Predictions on Apple App Store.

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README 

        
# Analytical Insights Into iOS App Profitability: A Comprehensive Study of the Apple App Store 📱💡



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## Introduction



In the dynamic world of mobile applications, a pressing question resonates among developers and investors: where should they channel their resources, and what elements truly dictate an app’s success? As 2021 came to a close, the App Store, with its impressive roster of 1.6 million iOS applications, stood out as a hub of innovation and potential, even in comparison to Google’s expansive Play Store with its 3.5 million apps.



This report embarks on an analytical journey into the second-largest app marketplace worldwide – Apple’s App Store. Here, we aim to unravel the myriad factors that determine an app’s trajectory, from its pricing and size to user ratings and categories. These aren’t just statistics; they form the foundation of strategic decision-making.



Our primary goal is straightforward: discern the pivotal variables that substantially impact an app’s market success and profitability. For stakeholders, understanding these nuances is essential, providing a compass in the intensely competitive landscape of app development and marketing. This exploration aims to convert data into actionable insights, offering a roadmap to success.



Join us in this analytical expedition, where data narratives guide our way, and each insight serves as a beacon for informed strategic choices. Step into a world where in-depth research becomes the linchpin to harnessing the vast potential of the mobile application universe.



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## Objective



The primary objective of this project is to analyze and predict the likelihood of mobile applications being free or paid on the Apple App Store. By leveraging machine learning models, the project aims to identify the key factors that influence an app's pricing model, such as:



- **App genre**

- **Size**

- **User ratings and reviews**

- **Release and update years**

- **Content rating**



This analysis will provide actionable insights to app developers and marketers, enabling them to make data-driven decisions to optimize app development, pricing strategies, and market positioning in the competitive app ecosystem.



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## SMART Questions



1. **Market Share Analysis:** We will explore which app categories and age groups dominate the market share on the Apple App Store.



2. **Installations Comparison:** Our study will investigate whether there is a discernible difference in the number of installations between free and paid apps, and whether free apps have an advantage.



3. **Developer Success Assessment:** We'll identify prolific developers and assess whether their creations consistently achieve success on the platform.



4. **Impact of Release Year on Ratings:** We'll analyze whether the release year of an app has a consequential impact on acquiring higher rating counts.



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## Dataset



The dataset used for this project can be found at [Apple App Store Dataset](https://www.kaggle.com/datasets/gauthamp10/apple-appstore-apps).



- Number of observations (roughly): **1,230,376**

- Number of features: **21**



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## GitHub Repository



To access the code and project files, visit our GitHub repository: [Project Repository](https://github.com/Srinivas39322/DATS_6101_11_GROUP_7).



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## Key Findings



1. **Addressing Skewness in Data:** Recognizing and addressing data skewness is crucial in real-world data analysis. This step is necessary to avoid developing models that inaccurately represent underlying patterns and trends, ensuring the validity of model predictions.



2. **Model Performance and Data Balance:** Models trained on unbalanced data may show high accuracy but can be misleading, often over-predicting the majority class. In contrast, models trained on balanced data provide a more truthful representation of predictive capabilities across all classes, even if this means a slight reduction in overall accuracy.



3. **Reliability and Continuous Improvement:** Emphasize reliability over raw accuracy in models. A balanced approach, despite potentially lower accuracy figures, reflects true model performance and is more trustworthy. The model development process should be iterative, focusing on continuous improvement and adaptation to enhance accuracy and dependability of predictions.



In conclusion, these insights underscore the importance of understanding and addressing data skewness in predictive modeling. By prioritizing balanced data and reliability over raw accuracy, and committing to an iterative process of improvement, we can develop more effective and trustworthy predictive models.