In this project, an exploration of reader and publisher patterns was conducted using a substantial dataset of Medium website from Kaggle. The primary aim was to extract meaningful insights and present them in a Python dashboard. The dataset underwent thorough preparation, including analysis with Python's Pandas Profiling library and data processing using Seaborn, Matplotlib, and NumPy. Additionally, data relationships were examined using a Neo4j graph database, seamlessly integrated into Python through cipher queries. This approach revealed intricate connections within the data, enriching the understanding of reader and publisher behaviors. This project exemplifies the potent synergy between data analysis, visualization, and graph databases in deciphering complex patterns and insights.
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This project examined Medium website data from Kaggle to understand reader and publisher patterns. It involved data analysis and visualization with Pandas Profiling, Seaborn, Matplotlib, and NumPy, and explored data relationships using Neo4j for insightful connections.
gauribhale12/Medium-Insights-Blogs-Analytics
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This project examined Medium website data from Kaggle to understand reader and publisher patterns. It involved data analysis and visualization with Pandas Profiling, Seaborn, Matplotlib, and NumPy, and explored data relationships using Neo4j for insightful connections.
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