Link: https://www.kaggle.com/datasets/chicago/chicago-crime
Data Cleaning & Preparation: -Load the dataset into Pandas and inspect it for missing values, incorrect data types, or irrelevant columns. -Remove unnecessary columns like case numbers or non-relevant identifiers. -Convert the date column into a proper datetime format and extract year, month, day, and hour. -Handle missing values by either removing them or filling them with appropriate values.
Data Visualization: -Crime Count by Type → Plot a bar chart of different crime types to see which are most frequent. -Crime Trend Over Time → Create a line chart showing the number of crimes per year/month. -Geospatial Analysis (Heatmap) → Use Folium to create a heatmap of crime locations to identify hotspots.
Identifying Relationships:- -Crime by Time of Day → Find if crimes peak during specific hours (e.g., night vs. day). -Crime by Location → Analyze the most common locations where crimes occur (e.g., streets, parks, stations). -Correlation Analysis → Use correlation heatmaps to see if there are patterns between crime type, time, and location.
Predicting Crime Hotspots: -Prepare a dataset with features like location, time, and crime type. -Split data into training and testing sets. -Use classification models (e.g., Random Forest, Decision Tree, or Logistic Regression) to predict crime-prone areas. -Evaluate model accuracy using confusion matrix and classification reports.
Conclusion & Insights: -Summarize key findings, such as: Most common crime types. High-crime locations and times. Any seasonal or yearly crime patterns. -Suggest possible preventive measures or policy recommendations based on the data...