Crime Detection Using Machine Learning Project

Crime detection using Machine Learning (ML) includes various situations like detecting future crime hotspots, understanding figures to support in current investigations and identifying criminal behaviors in the real-world using supervision data. More than 18+ years we are giving 360-degree support to scholars on what type of research issues that they face. We have done many Crime Detection Machine Learning Project for a past few years. Customised project work are done for scholars right from topic selection, research proposal, synopsis, paper writing and paper publishing for their crime detection project.

The following are the common steps that we implement to develop a crime prediction project using ML:

  1. Define Objective:
  • We interpret what aspect of crime prediction we are aiming on: forecasting crime rates, finding crime patterns, real-time monitoring analysis, etc.
  1. Data Collection:
  • Existing Datasets: Few public datasets such as the FBI’s crime datasets and city-specific crime datasets like the Chicago Crime dataset are helpful while beginning our work.
  • Surveillance Data: When we work on real-time observations, we should access the monitor footage that increases security issues.
  • Collaboration: For enabling authority to previous crime data and context we commit with local law enforcement.
  1. Pre-processing the Data:
  • Handle Missing Values: While working with public datasets, we remove the lost values and discontinuous data.
  • Feature Engineering: We obtain the latest features which offer understanding like temporal designs such as time of day, day of the week, season and dimensional figures like proximity to significant landmarks.
  1. Exploratory Data Analysis (EDA):
  • We visualize crime dispersion throughout various areas, durations and demographics.
  • By detecting some directions and figures in the data we assist the modeling process.
  1. Model Choosing & Training:
  • Prediction: Regression frameworks, time series models such as ARIMA and we utilize more advanced techniques like LSTM for detecting crime rates.
  • Classification and Pattern Recognition: Techniques such as Decision Trees, Random Forests, Gradient Boosting Machines and Neural Networks are beneficial in our project.
  • Real-time Monitoring: To detect malicious events and persons from video content we use object prediction frameworks such as YOLO and Faster R-CNN.
  1. Evaluation:
  • Based on the task various metrics are identical. For forecasting, we use MAE and RMSE are adaptable. We employ metrics such as precision, recall and F1-score for classification.
  • We often evaluate the framework by using single test data and through cross-validation.
  1. Deployment:
  • Combining our model into a mechanism which serves for law enforcement and protection agencies. We utilize this as a dashboard for simulating detected crime hotspots and a real-time notification system in supervising applications.

Project Extensions:

  • Real-time Tracking: To track public areas in real-time and notify officials of fraudulent behaviors we incorporate a system.
  • Social Media Monitoring: To determine the possible attacks and criminal intentions we observe social media data.
  • Community Review Integration: We allow group members to report events and offer notes for combining it with our model.


  • Data Sensitivity: When we use surveillance data, crime data is susceptible and there is some trouble in security.
  • Ethical Concerns: There is a danger of unfairness in the data that leads to inequitable experiences and it is important to periodically check models and make sure of fairness.
  • Dynamic Nature: We require consistent updating and supervising in our model, because the crime designs emerge fast.

       Consultation with area professionals such as law enforcement officers and crime research experts is essential. By this we gain context, steps for choosing features and help us to validate the experimental similarities and accuracy of our model.

Share all requirements of your project details to us and we will update to you within turn around time. We will craft the work by collaborating with scholars and produce research end results as per your specifications. Research Manuscript will be specifically crafted by professionals as per scholars needs without any errors and plagiarism free paper will be issued.

Crime Detection using Machine Learning Project Topics

Crime Detection Using Machine Learning Project Thesis Topics

Some of the thesis topics that we have suggested are listed below read it and get inspired by our work. Get thesis proposal done at on crime detection ML area. Our professionals stay alert on updated techniques and we have massive resources so that we propose trending ideas and topics in ML. Our thesis writers will assist you in all PhD and MS thesis work.

  1. Crime Hotspot Detection using Optimized K-means clustering and Machine Learning Techniques


K-Means Clustering, Elbow Method, Support Vector Machine, Random Forest, Decision Tree, UCI Crime dataset

            A data mining and ML techniques are employed in our article to detect the crimes. We examined the crimes by utilizing methods including optimized K-means clustering and the elbow technique. Our framework comprises of various categorization methods such as Decision Tree, Support Vector Machine, and Random Forest. We trained this framework by utilizing the specified dataset to analyze and detect the crime areas.

  1. Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning


Sentiment analysis, Naive Bayes, LSTM, crime-intent, criminal, cyberbullying

            A crime related sentiment analysis is evaluated in our study by utilizing ML approaches. We carried out comparative study for twitter sentiment analysis by considering various datasets. We utilized several techniques such as Support Vector Machine (SVM), Naive Bayes, and Long Short-Term Memory (LSTM). From the analysis, LSTM provides better end results than others. We state that, our suggested approach efficiently detects the crime tweets.

  1. Suspicious Crime Identification and Detection Based on Social Media Crime Analysis Using Machine Learning Algorithms


Suspicious crime identification, Crime detection, social media crime analysis, Feature selection, Machine learning

            To detect the doubtful information that are uploaded on Facebook and to forecast the crime rate is an ultimate aim of our approach. We pre-processed data by removing missing and repeated values, and eliminating stop words from doubtful posts. We clustered the social media post to detect the suspicious one. We utilized feature selection method to choose important features based on the crimes. We employed categorization methods to forecast the crime.

  1. Crime Detection and Analysis from Social Media Messages Using Machine Learning and Natural Language Processing Technique


Social media, Natural language processing

            An innovative framework is recommended in our paper to evaluate and identify the crime rates in social media comments and postings. By utilizing our framework, we identified the assaults and comments that are based on drug dealings, hateful and disrespectful contents. We employed Natural language processing for the purpose of text tokenization, stemming and lemmatisation. As a result, SVM offers better outcomes than RF in crime identification process.

  1. A supervised machine learning framework with combined blocking for detecting serial crimes


Serial crime detection, Classification, pairwise calculation, Blocking, Class imbalance

            A combined Blocking (CB) model is suggested in our study. We constructed a block by describing criminal behaviour as a behaviour key (BHK). CB built a final blocking strategy by integrating various weak blocks and the final blocking strategy comprises of several behaviour keys. We inserted the CB into supervised ML model. We conclude that, our suggested model enhances the identification process and minimize the pairwise comparison of crimes.

  1. Deep Neural Network-based Crime Scene Detection with Frames


Deep neural network, convolutional neural network, crime scene, Tensor flow

            To detect and forecast the crime by considering several factors such as blood, gun and knife in the obtained images, we recommended a DNN approach in our project. We utilized DNN’s Non-linearity ReLu (Rectified Linear Unit), a Convolutional Neural Network Layer, a Fully Connected Layer, and a Dropout Function to obtain the crime identification findings. We utilized Tensorflow platform to acquire the results from neural network.

  1. Crime Detection Using Multi-Layer Perceptron in Social Media Platforms


Social Media Platforms (SMPs), Neural Network, Data Breaches, Multi-Layer Perceptron and Ontology

            An ontology related multilayer perceptron (MLP) classifier a feed forward artificial neural network technique (MLP-NN) is proposed in our research to detect the criminal based activities in social media (SM) platforms. Our approach develops a program to automatically classify the uploaded posts related to crimes. We evaluated and compared our approach with previous research and we demonstrate that, the proposed work efficiently detects SM crimes.

  1. A Novel Framework for Crime Anomaly Detection using Convolution Auto Encoder


Artificial Intelligence, Image Processing, Deep Learning, Decision Making, Data Analysis, Neutrosophic Set

            A Convolution Auto Encoder framework is suggested in our article that is constructed with ConvLSTM layers for crime detection. Our framework splits into three sectors; they are encoder, latent space, and decoder. We utilized various optimizers such as Adam, Adadelta and RMS Prop with the learned models. To find out an optimal optimizer, we performed comparative analysis in terms of various metrics. In that, RMSProp optimizer achieved better results.

  1. Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behaviour


Financial crime, outlier detection, fraud prediction, credit card fraud, non-performing assets

            Our paper constructed supervised learning frameworks including DT, RF, and KNN to predict the customer’s loyalty in banking fields. We find out the optimal framework by comparing the results in terms of performance metrics. A major goal of our approach is to minimize the banking sector’s non-performing assets (NPA) by minimizing false positive parameters. To understand the client’s behaviour by bankers, we graphed the data.

  1. Crime identification and detection using Machine Learning


            To predict the kinds of crimes that are happened in our surroundings at a specified period of time is the main aim of our research. We considered AI and ML techniques as the best approaches to identify and obstruct the crimes. We examined the crime related data by employing various methods such as Naive Bayes, K-nearest neighbour, and linear regression. Results show that, Naive Bayes method outperformed other ML methods.

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