Team members:
- Dhisha C S
- Rohith Krishna R
- V K Deeksha
This project focuses on analyzing crime data in Los Angeles from 2020 to the present. It explores crime trends, patterns, and hotspots using data mining techniques such as classification, clustering, and time series analysis. The goal is to provide meaningful insights for better understanding urban crime behavior.
The dataset was collected from the Los Angeles Open Data Portal (Data.gov) and also supports local file uploads. The data was cleaned by handling missing values, removing duplicates, and engineering features like victim age, area, and weapon used. Categorical variables were encoded to prepare the data for modeling.
● EDA (Exploratory Data Analysis) was done using plots and summaries to understand key crime statistics. Time Series Analysis was performed to study crime trends over time.
● MODELS: Classification was implemented using a Decision Tree Classifier to predict crime categories. Clustering was performed using K-Means(cluster based on weapons) and AGNES Hierarchical Clustering to group similar crime incidents and analyze crime hotspots.
● Python,Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
● Streamlit for building an interactive web application
This project analyzes LA crime data and visualizes patterns.
