Social Network Analysis of Disinformation, Platforms, Freelancing around Amber Heard, Johnny Depp, Elon Musk - Twitter, Reddit, YouTube, Instagram, Change.org, Facebook, Tumblr, TikTok. To create Foundations to Help victims of abuse, retaliation, domestic abuse, coercive control, crime, & Hollywood Research. We want to Save Lives & Help partners

Lectures on "crime and political corruption analysis using data mining, machine learning and complex networks" at the School of Applied Mathematics in the Institute of Mathematics and Computer Science at University of São Paulo

Association Rule Mining from Spatial Data for Crime Analysis

Federal Crime Data Standardization and Analysis — The Trace and BuzzFeed News

A Go port of FollowTheMoney (FtM) — a pragmatic data model for people, companies, assets, relationships and documents used in investigative work and financial crime analysis.

A data analysis and visualization project that explores crime patterns in US using Python, pandas, seaborn, and matplotlib.

No description available

Conducted data analysis, statistical analysis, and data visualization on an Indian crime dataset. Applied various machine learning algorithms to gain insights from the data. Utilized Time-Series models for prediction and forecasting based on the crime data analysis.

[Data Science] Chicago Crime data analysis from 2001 to present.

No description available

Crime data processing, analysis, and visualization. Minneapolis only at the moment.

Problem Statement The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset. Motivation Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. Sites such as Twitter and Facebook have been seeking to actively combat hate speech. In spite of these reasons, NLP research on hate speech has been very limited, primarily due to the lack of a general definition of hate speech, an analysis of its demographic influences, and an investigation of the most effective features. Data Our overall collection of tweets was split in the ratio of 65:35 into training and testing data. Out of the testing data, 30% is public and the rest is private. Data Files train.csv - For training the models, we provide a labelled dataset of 31,962 tweets. The dataset is provided in the form of a csv file with each line storing a tweet id, its label and the tweet. There is 1 test file (public) test_tweets.csv - The test data file contains only tweet ids and the tweet text with each tweet in a new line.

A Critical Assessment of the Big Data Approach to Violent Crime Analysis during Lockdowns

No description available

CRIME DATA ANALYSIS TREND

No description available

This is my Mini project of MTech. In this project, Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used.

Regression analysis using python and scikit learn - Communities and Crime Data Set (UCI).

No description available

📊 Data-Driven Crime Analysis A project that uses data analytics and machine learning to analyze crime patterns, predict high-risk areas, and visualize trends. It helps support smarter crime prevention and public safety planning using real-world crime datasets.

Code Examples for Data Science for Crime Analysis with Python book

This repository contains the collection of Python and Javascript (Observable Notebook) projects made for the DTU Data Science course 02806: Social Data Analysis and Visualizations

A data analysis project on a Crime Against Women dataset.

Provider Fraud is one of the biggest problems facing Medicare. According to the government, the total Medicare spending increased exponentially due to frauds in Medicare claims. Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims. Rigorous analysis of Medicare data has yielded many physicians who indulge in fraud. They adopt ways in which an ambiguous diagnosis code is used to adopt costliest procedures and drugs. Insurance companies are the most vulnerable institutions impacted due to these bad practices. Due to this reason, insurance companies increased their insurance premiums and as result healthcare is becoming costly matter day by day. Healthcare fraud and abuse take many forms. Some of the most common types of frauds by providers are: a) Billing for services that were not provided. b) Duplicate submission of a claim for the same service. c) Misrepresenting the service provided. d) Charging for a more complex or expensive service than was actually provided. e) Billing for a covered service when the service actually provided was not covered. Problem Statement The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them. Along with this, we will also discover important variables helpful in detecting the behavior of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behavior of providers. About the dataset : For the purpose of this project, we are considering Inpatient claims, Outpatient claims and Beneficiary details of each provider. Lets s see their details : A) Inpatient Data This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code. B) Outpatient Data This data provides details about the claims filed for those patients who visit hospitals and not admitted in it. C) Beneficiary Details Data This data contains beneficiary KYC details like health conditions, Regio region they belong to etc.

crime data analysis website

The Crime Analysis Toolbox contains a series of ArcGIS geoprocessing tools and models for identifying and analyzing crime incident data

Exploratory data analysis on New York Crime Complaint Data

Data analysis project using Python on NCRB Crime in India 2020 dataset. Includes data cleaning, visualization, and insights with Pandas, Seaborn, and Matplotlib.

To conduct a comprehensive data analysis of the 'USArrests' dataset in R, with the primary goal of exploring the relationship between urbanization and crime rates across US states. The project will leverage foundational R functionalities to perform data exploration, visualization, and hypothesis testing.

This is a comprehensive guide to using the FBI's Uniform Crime (UCR) Reporting Program Data, a collection of crime and arrest datasets which are often referred to as UCR data.