├── Python final.zip
└── README.md


/Python final.zip:
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https://raw.githubusercontent.com/Prakhar2402/INT-375-Python-Project-Crime-Mapping/HEAD/Python final.zip


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/README.md:
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  1 | # INT-375-Python-Project-Crime-Mapping :
  2 | Project Overview
  3 | This project performs Exploratory Data Analysis (EDA) on real-world crime data to uncover meaningful insights and patterns. Using Python libraries like Pandas, Matplotlib, Seaborn, and Folium, the project visualizes:
  4 | 
  5 | Temporal crime trends
  6 | 
  7 | Geographical hotspots
  8 | 
  9 | Crime type distributions
 10 | 
 11 | Victim demographics
 12 | 
 13 | Resolution status of cases
 14 | 
 15 | These insights aim to support data-driven decisions for public safety and law enforcement resource planning.
 16 | 
 17 | 📂 Dataset Information :-
 18 | Name: Crime Data from 2020 to Present
 19 | 
 20 | Source: Data.gov
 21 | 
 22 | Format: Excel (.xlsx)
 23 | 
 24 | Cleaned and preprocessed for better usability
 25 | 
 26 | 📑 Key Features in Dataset:
 27 | DATE OCC, TIME OCC: Time of the incident
 28 | 
 29 | AREA NAME, LAT, LON: Location-based data
 30 | 
 31 | CRM_CD_DESC: Crime type
 32 | 
 33 | VICT AGE, VICT SEX, VICT DESCENT: Victim details
 34 | 
 35 | WEAPON DESC: Weapon used (if any)
 36 | 
 37 | STATUS DESC: Case resolution status
 38 | 
 39 | 🧰 Libraries & Tools Used :-
 40 | pandas – Data manipulation and analysis
 41 | 
 42 | numpy – Numerical operations
 43 | 
 44 | matplotlib & seaborn – Visualizations
 45 | 
 46 | folium – Interactive geographic maps
 47 | 
 48 | plotly – Advanced, interactive charts
 49 | 
 50 | 📊 Features & Analysis Performed
 51 | 1️⃣ Crime Distribution and Trends Over Time
 52 | Grouped by year, month, and hour
 53 | 
 54 | Found peak hours: 6 PM to 12 AM
 55 | 
 56 | Higher crime in summer months and weekends
 57 | 
 58 | Visualizations: Line plot, histogram, heatmap
 59 | 
 60 | 2️⃣ Geographic Crime Hotspots
 61 | Mapped high-crime areas using Folium HeatMap
 62 | 
 63 | Identified districts like 77th Street with higher incidents
 64 | 
 65 | Visualizations: Bar chart, interactive map (saved as crime_hotspots_map.html)
 66 | 
 67 | 3️⃣ Crime Type and Weapon Analysis
 68 | Common crimes: Theft, Assault, Burglary
 69 | 
 70 | Frequent weapons: Handguns, Knives
 71 | 
 72 | Visualizations: Bar plots, pie charts
 73 | 
 74 | 4️⃣ Victim Demographics
 75 | Most victims aged 20–40
 76 | 
 77 | Slightly more male victims
 78 | 
 79 | Affected ethnicities: Hispanic, Black
 80 | Visualizations: Histogram, gender/ethnicity bar plots
 81 | 
 82 | 5️⃣ Resolution Status
 83 | Majority marked as Pending Investigation
 84 | 
 85 | Some crimes like Robbery have low clearance
 86 | 
 87 | Visualizations: Donut chart, stacked bar chart
 88 | 
 89 | 📌 Sample Outputs Which will be displayed
 90 | 
 91 | 📈 Temporal Trends
 92 | 
 93 | 🌍 Heatmaps of Crime Locations
 94 | 
 95 | 📊 Top Crime Types & Weapons
 96 | 
 97 | 👤 Demographic Analysis
 98 | 
 99 | ✅ Solved vs Unsolved Crime Stats
100 | 


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