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that influence the duration of a by-law investigation:use the indate, noticedate, nextindate, and expirydate features to calculate the time taken for eachinvestigation -> analyze whether factors like the type of investigation (intype), issue severity (issue), orthe presence of deficiencies (deficiencies table) affect the time features.(2): the geographic patterns in where by-law infractions occur within t orontouse features like addrline, house, street, type from the address file to map the investigations spatially..and map them to the corresponding ids in deficiencies and investigation tables to identify areas prone tocertain types of infractions.(3): t o predict the outcome of an investigation based on the initially gathered information in features likecomplaint type and address (geographical info)use features like intype, desc #deficiencies, and location or address in address file to classifyinvestigations into outcomes (like whether they r violation found or no violation found\u2026 this is theresponse variable) ##we can use classifications and regression models for this!!!!!(4) do certain times of the year see more infractions/", "Sessions": 1, "Users": 1, "Views": 3, "Avg Session Duration (Sec)": 36, "Views per Session": 3, "Bounce Rate %": 0, "Month": "202409"},{"_id": 13021, "Link Source -Page URL": "open.toronto.ca/dataset/surface-routes-and-schedules-for-bustime/download/surfacegtfs.zip/", "Sessions": 1, "Users": 1, "Views": 1, "Avg Session Duration (Sec)": 363, "Views per Session": 1, "Bounce Rate %": 0, "Month": "202409"},{"_id": 13022, "Link Source -Page URL": "open.toronto.ca/dataset/traffic-volume-counts-automated/", "Sessions": 1, "Users": 1, "Views": 1, "Avg Session Duration (Sec)": 26, "Views per Session": 1, "Bounce Rate %": 0, "Month": "202409"},{"_id": 13023, "Link Source -Page URL": "open.toronto.ca/dataset/4b5c7a84-dea1-4137-875d-71d7f662c83f/", "Sessions": 1, "Users": 1, "Views": 1, "Avg Session Duration (Sec)": 1672, "Views per 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