future work

notebooks/FinalMilestone.ipynb

@@ -120491,16 +120491,21 @@
    "id": "e6d691c5-349f-4a11-b172-f27753329f59",
    "metadata": {},
    "source": [
-    "Our Generalized Addititive Models do an effective job of fitting to the data, as demonstrated above by the psuedo R-square parameter. Perhaps this is due to our high effective degrees of freedom, which gets into the trade-off of bias versus variance, but we attempted to minimize that as much as we could. Poisson GAM was our most effective model due to the Poissonian error distribution conforming nicely to the errors associated with a strictly-positive count variable. We are able to explain much of the variance when it comes to the violent crime counts per day based on the temperature, but the predictive utility is obviously not very good. This is expected, as we were primarily looking for a relationship between weather factors and crime, but not intending to do crime forecasting. That is an entire area of research that would require much more data than the weather on a given day. "
+    "Our Generalized Addititive Models do an effective job of fitting to the data, as demonstrated above by the psuedo R-square parameter. Perhaps this is due to our high effective degrees of freedom, which gets into the trade-off of bias versus variance, but we attempted to minimize that as much as we could. Poisson GAM was our most effective model due to the Poissonian error distribution conforming nicely to the errors associated with a strictly-positive count variable. We are able to explain much of the variance when it comes to the violent crime counts per day based on the temperature, but the predictive utility is obviously not very good. This is expected, as we were primarily looking for a relationship between weather factors and crime, but not intending to do crime forecasting. That is an entire area of research that would require much more data than the weather on a given day."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "48c9cc01",
    "metadata": {},
    "source": [
-    "Not finished yet..."
+    "Future work for this project would definitely be feature scaling. The NOAA dataset proved extremely useful for temperatures and some other features, but we would definitely want to look at more factors like humidity, wind speeds, etc. The study our model was based on from Oslo focused their efforts on encompassing the weather to the fullest, including more factors than we currently do in our project. Then again, New Orleans is hot and humid for a majority of the year, so more future work would definitely bring in data from other cities. It would be fascinating to look at this trend with data from the entire country, but this is sadly unfeasible for us at the moment."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {