The COVID-19 Data Analysis Project is a comprehensive examination of the global impact of the COVID-19 pandemic. In the wake of this unprecedented global crisis, our project seeks to shed light on the pandemic's spread, effects, and the data-driven insights that can help in understanding and mitigating its impact.
This project aims to analyze the COVID-19 pandemic using publicly available data. The project includes a Jupyter notebook with Python code to extract, clean, and visualize COVID-19 data from various sources. Additionally, the project provides a dashboard to explore the data interactively.
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Used Pandas and Json to gather data from API and then data cleaning.
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For K.P.I.’s and insights Used SQL to get useful data sets.
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Then transferred datasets to Excel for visualization.
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For the presentation used M.S. PowerPoint with the help of team members.
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Import the data from API using the requests library.
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The imported data was in JSON format hence we used JSON library to read the data.
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We looked for null values and replaced them with zero, looking for duplicates.
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Stated analyzing the data by using pandas functions like group by, sort_values, etc.
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Used nested 'for' loops to extract the relevant data from the nested dictionary.
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Extracted the individual state data from the data frame in CSV format and imported data into MySQL.
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Aggregated the distribution by month and week wise for each state.
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Imported the aggregated data into Excel for further Analysis.
Data Extraction from JSON : This process involves parsing structured JSON data to access, manipulate, and analyze valuable information.
Data Cleaning and Pre-Processing : Real-World data is inconsistent and unorganized.
Creating an Interactive Dashboard : Creating an interactive dashboard to understand data effectively.
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The analysis focused on the weekly progression of COVID-19 cases, recoveries, deaths, and tests, providing valuable insights into the pandemic's impact across various regions and timeframes.
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Fluctuations in the number of cases and deaths were observed, underscoring the dynamic nature of the pandemic's effects in different geographical areas.
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Through effective data visualization using charts and graphs, the project facilitated a clearer understanding of the data, aiding in the interpretation of trends and patterns.
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The project's findings hold practical significance for public health authorities, enabling them to devise more targeted and efficient strategies for containing the virus's transmission.
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Policymakers can benefit from the analysis by making informed decisions on resource allocation, directing support to regions experiencing the highest impact from the pandemic.