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analytics, country-specific insights, and individual company details.\r\n\r\n\r\n- Handling missing or null values in the dataset, converting data types  (dates to datetime objects).\r\n- Perform basic exploratory analysis to understand the structure and characteristics of the data.\r\n- Exploring summary statistics, distributions, and correlations between variables.\r\n- Creating visualizations to gain insights into the layoff data.  \r\n- Utilizing plots such as bar charts, line charts, and choropleth maps to represent the data effectively.\r\n- Developing an interactive dashboard using tools like Streamlit\r\n- Allowing users to explore the layoff data dynamically through dropdowns, sliders, or search bars\r\n\r\n  Link: https://layoff-data-analysis.streamlit.app/ \r\n \r\n----------------------------------------------------------------\r\n\r\n![Screenshot 2024-04-21 135228](https://github.com/bhavanachitragar/Layoff_Analysis/assets/91766461/5587cfd1-ebfe-4761-8b50-7965e903dd61)\r\n![Screenshot 2024-04-21 135202](https://github.com/bhavanachitragar/Layoff_Analysis/assets/91766461/b741ed51-bb4f-4530-9658-269015f77342)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhavanachitragar%2Flayoff_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbhavanachitragar%2Flayoff_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhavanachitragar%2Flayoff_analysis/lists"}