The document discusses using machine learning algorithms to predict crime type and occurrence based on location and time using crime data. It proposes using linear regression, decision tree, and random forest models which tended to give good accuracy in previous works. The system would take crime types as input and output the areas where crimes are committed. It claims this approach provides high prediction accuracy compared to existing methods.

1. CRIME TYPE AND OCCURRENCE PREDICTION
USING MACHINE LEARNING ALGORITHM
PRESENTED BY

2. ABSTRACT
• In this era of recent times, crime has become an evident way of making people and society under trouble. An
increasing crime factor leads to an imbalance in the constituency of a country.
• In order to analyse and have a response ahead this type of criminal activities, it is necessary to understand the
crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle
open source which in turn used for the prediction of most recently occurring crimes.
• The major aspect of this project is to estimate which type of crime contributes the most along with time period
and location where it has happened. Some machine learning algorithms such as XGBoost,KNN is implied in
this work in order to classify among various crime patterns and the accuracy achieved was comparatively high
when compared to precomposed work.

3. INTRODUCTION
• Crime has become a major thread imposed which is considered to grow relatively high in intensity.
An action stated is said to be a crime, when it violates the rule, against the government laws and it
is highly offensive. The crime pattern analysis requires a study in the different aspects of
criminology and also in indicating patterns.
• It imposes the uses of existing crime data and predicts the crime type and its occurrence bases on
the location and time.
• Researchers undergone many studies that helps in analyzing the crime patterns along with their
relations in a specific location. Some of the hotspots analyzed has become easier way of classifying
the crime patterns.
• In this proposed one to impose a machine learning algorithms to find the matching criminal patterns
along with the assist of its category with the given temporal and spatial data.

4. EXISTING SYSTEM
• k-NN, RF, SVM and Bayes models are existing methods Although studies have been done in the medical field
with an advanced data exploration using machine learning algorithms, orthopedic disease prediction is still a
relatively new area and must be explored further for the accurate prevention and cure. it mines the double
layers of hidden states of vehicle historical trajectories, and then selects the parameters of Hidden Markov
Model(HMM) by the historical data.
• In addition, it uses a Viterbi algorithm to find the double layers hidden states sequences corresponding to the
just driven trajectory. Finally, it proposes a new algorithm for vehicle trajectory prediction based on the
hidden Markov model of double layers hidden states, and predicts the nearest neighbor unit of location
information of the next k stages

5. DISADVANTAGES OF EXISTING SYSTEM
• By this methodology they had less accuracy in prediction.
• By this methodology results are not perfect.

6. PROPOSED SYSTEM
• The proposed system is made on the basis of the research work that is done by going through various such
documentations.
• Nearly all of the crimes are predicting based on the location and the types of crimes that are occurring in those areas.
• On surveying previous works, Linear Regression, Decision Tree and Random Forest tend to give good accuracy so
these models are used in this paper to predict crimes.
• This paper takes types of crimes as input and gives the area in which crimes are committed as output. The data pre-
processing involves data cleaning, feature selection, dropping null values, data scaling by normalizing and
standardizing.
• After data pre-processing the data is free of null values which m ay alter the accuracy of the model significantly and
feature selection is used to select only the required features that won’t affect the accuracy of model

7. ADVANTAGES OF PROPOSED SYSTEM
• We had a high accuracy in this model prediction methodology. In this algorithm for a data mining
approach to help predict the crimes patterns and fast up the process of solving crime.
• The results are perfect and accurate using this technology.

8. SYSTEM ARCHITECTURE

9. HARDWARE REQUIREMENTS
• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 4 GB

10. SOFTWARE REQUIREMENTS
 Operating system : Windows 10.
 Coding Language : Python 3.8
 Web Framework : Flask

11. REFERENCE
[1] Suhong Kim, Param Joshi, Parminder Singh Kalsi,Pooya Taheri, “Crime Analysis Through Machine
Learning”, IEEE Transactions on November 2018.
[2] Benjamin Fredrick David. H and A. Suruliandi,“Survey on Crime Analysis and Prediction using Data
mining techniques”, ICTACT Journal on Soft Computing on April 2012.
[3] Shruti S.Gosavi and Shraddha S. Kavathekar,“A Survey on Crime Occurrence Detection and
prediction Techniques”, International Journal of Management, Technology And Engineering , Volume 8,
Issue XII, December 2018.
[4] Chandy, Abraham, "Smart resource usage rediction using cloud computing for massive data
processing systems" Journal of Information Technology 1, no. 02 (2019): 108-118.
[5] Learning Rohit Patil, Muzamil Kacchi, Pranali Gavali and Komal Pimparia, “Crime Pattern Detection,
Analysis & Prediction using Machine”, International Research
Journal of Engineering and Technology, (IRJET) e-ISSN: 2395-0056, Volume: 07, Issue: 06, June
2020
[6] Umair Muneer Butt, Sukumar Letchmunan, Fadratul Hafinaz Hassan, Mubashir Ali, Anees Baqir
and Hafiz Husnain Raza Sherazi, “Spatio-Temporal Crime Hotspot Detection and Prediction: A
Systematic Literature Review”, IEEE Transactions on September 2020.