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The given dataset covers the period from 2016 to 2022, including details on crimes based on Montgomery County’s UCR rules and provides details on reported crimes.\t\n\nThe primary objective is to analyse patterns in crime occurrences considering various factors in Montgomery County. By examining the dataset, aim is to identify the types of crimes, their frequencies, and potential categorizations. \n\nThe study utilizes statistical and data analysis techniques to visualise trends, categorization, and areas for potential intervention. Through these visualizations, the research seeks to provide actionable recommendations to Montgomery County's local police agencies, contributing to enhanced public safety.\n\n # Key Tasks \n •\tCleaned and preprocessed large datasets from the National Incident-Based Reporting System (NIBRS).\n\n•\tConducted exploratory data analysis (EDA) and created visualizations to identify crime trends and patterns.\n\n•\tPerformed statistical analysis to highlight high-crime areas and seasonal trends.\n\n•\tDeveloped intuitive visualizations to effectively communicate findings to stakeholders.\n\n# Common Libraries used \n\n•\tNumpy\n\n•\tPandas\n\n•\tSeaborn\n\n•\tMatplotlib\n\n\n\n# Most used functions and object \n       \n•\tNp.where() – For grouping and merging data.\n\n•\tValue_counts() – to find the frequency of the item\n\n•\tCountplot() – to plot the bar graph(X, Y axis and hue attributes changes according to the requirement).\n\n•\tContainer object – to present the precise count for each item and enhancing the granularity of the visualization. \n\n•\tColor palette – is used for choosing the custom colour palette.\n\n•\tScatterplot() – to visualize the pictorial representation and maps.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanushkundu%2Fcrime-pattern-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanushkundu%2Fcrime-pattern-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanushkundu%2Fcrime-pattern-analysis/lists"}