This blog post focuses on analyzing the Chicago Crime rate from 2001 to 2016 and predicting crime rate statistics in 2017 given the historical trend. Chicago is one of the largest cities in the US, and the city’s overall crime rate, especially the violent crime rate, is higher than the US average. Dataset has been obtained from the Chicago Police Department’s CLEAR (Citizen Law Enforcement Analysis and Reporting) system and can be downloaded here. The dataset contains 7,941,282 entries for 16 years. The Dataset contains the following columns:
In this post, I would like to discuss how to approach a Data Science (DS) project as a beginner. When I started my journey in DS, I used to watch various videos and read numerous blogs online about different DS projects. I used to be baffled by the approach used by the author of the project, as I had never thought to analyze the data as they did. I used to ask myself how can I develop this intuition so that I can also perceive the trend in the data in a similar way.
It is said that practice makes…
Every newbie programmer encounters the word Git, but very few of them commit to it, pun intended. Git is difficult to understand for someone new, not because it is something complicated but because they are not able to understand the use of it. This post will help you understand the scenarios where you might encounter the use of the most common and most basic git commands. This is an introductory post, meant for absolute beginners who want to understand what git is all about.
In this post, I present a step-by-step guide to implement and deploy your own Mask RCNN model. I referred to a lot of blogs online when I created my own model for deployment, few blogs used images annotated with bounding boxes and single class classification, some used bounding box annotated images and multiple class classification, and others used polygon annotations with single-class classification. This post will provide the code and its explanation for all these scenarios. The flow of the post will be as follows: