Crime Rate Prediction using Facebook Prophet

Crime Rate Prediction using Facebook Prophet

A Guideline to Make the Best Use of FB Prophet for Time Series Forecasting. Time series prediction is one of the must-know techniques for any data scientist.

Time series prediction is one of the must-know techniques for any data scientist. Questions like predicting the weather, product sales, customer visit in the shopping center, or amount of inventory to maintain, etc - all about time series forecasting, making it a valuable addition to a data scientist’s skillsets.

In this article, I will introduce *how to use Facebook Prophet to predict the crime rate in Chicago. *Split into 5 parts:

1. Prophet Introduction

2. EDA

3. Data processing

4. Model prediction

5. Takeaways

Let’s begin the journey.

1. Prophet Introduction

In 2017, Facebook Core Data Science Team open-sourced Prophet. As stated on its Github page, Prophet is:

  • a procedure for forecasting time series data;
  • based on additive models;
  • fit non-linear trends with yearly, weekly, and daily seasonality, plus holiday effect.

Prophet uses a decomposable model with three main components, including trend, seasonality, and holidays, as combined below:

Image for post

prophet time-series-forecasting python3 data-science data analysis

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

A Real-World Time Series Data Analysis and Forecasting

Applying the ARIMA model to forecast time series dataThe notion of stationarity of a series is important for applying statistical forecasting models since.

Simple Multivariate Time-Series Forecasting

This tutorial was supposed to be published last week. Except I couldn’t get a working (and decent) model ready in time to write an article about it.

Time Series Forecasting: Limitations of LSTMs

While LSTMs have become increasingly popular for time series analysis, they do have limitations. Long-short term memory networks (LSTMs) are now frequently used for time series analysis.

Why Does Stationarity Matter in Time Series Analysis?

Learn the Fundamental Rule of Time Series Analysis: Stationarity is an important concept in the field of time series analysis with tremendous influence on how the data is perceived and predicted.

Demand Forecasting using FB-Prophet

Implementing a Simple and Effective method for Demand Forecasting. Forecasting future demand is a fundamental business problem and any solution that is successful in tackling.