├── README.md
└── main.py


/README.md:
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 1 | # LAPD Crime Data Analysis Tool
 2 | 
 3 | This project performs exploratory data analysis (EDA) and visualizations on crime data from Los Angeles, spanning from 2020 to the present. The tool allows users to interactively explore crime trends and patterns by time and location, and the most common types of crimes reported.
 4 | 
 5 | ## 📂 Dataset
 6 | 
 7 | - **Source**: [Crime Data from 2020 to Present](https://data.lacity.org/)
 8 | - **Format**: CSV
 9 | - **Columns Used**:
10 |   - `Date Rptd`: Date the crime was reported
11 |   - `TIME OCC`: Time of occurrence (HHMM format)
12 |   - `Crm Cd Desc`: Description of the crime
13 |   - `AREA NAME`: Name of the reporting LAPD area
14 | 
15 | > Make sure the dataset is downloaded and the path is correctly set in the script:
16 | ```python
17 | DATA_PATH = "C:\\Users\\hp\\Downloads\\Crime_Data_from_2020_to_Present.csv"
18 | ⚙️ Features
19 | ✔️ Data Cleaning
20 | Drops rows with missing values in key columns.
21 | 
22 | Converts Date Rptd to datetime.
23 | 
24 | Formats TIME OCC into 4-digit strings for consistency.
25 | 
26 | ✔️ Visualizations
27 | Top Crimes: Bar chart of the 10 most common crimes.
28 | 
29 | Crime Trend Over Time: Monthly line graph of reported crimes.
30 | 
31 | Crimes by Area: Bar chart of top 10 areas with highest crime counts.
32 | 
33 | Hourly Crime Distribution: Line graph showing number of crimes by hour.
34 | 
35 | Area vs Crime Heatmap: Heatmap showing the frequency of top crime types across top LAPD areas.
36 | 
37 | ✔️ Interactive Menu
38 | Run-time console-based interface for exploring various aspects of the data without modifying the code.
39 | 
40 | ▶️ Usage
41 | Install required libraries (if not already installed):
42 | 
43 | bash
44 | Copy
45 | Edit
46 | pip install pandas numpy matplotlib seaborn
47 | Update the DATA_PATH in the script with your dataset path.
48 | 
49 | Run the script:
50 | 
51 | bash
52 | Copy
53 | Edit
54 | python lapd_crime_analysis.py
55 | Choose whether to enter interactive mode or display all visualizations automatically:
56 | 
57 | vbnet
58 | Copy
59 | Edit
60 | Do you want to run interactive analysis? (y/n):
61 | 📊 Example Visuals
62 | Top 10 Most Reported Crimes
63 | 
64 | Crime Trends Over Time
65 | 
66 | Note: Replace image URLs with your own if needed.
67 | 
68 | 📁 Folder Structure
69 | bash
70 | Copy
71 | Edit
72 | lapd_crime_analysis/
73 | │
74 | ├── lapd_crime_analysis.py       # Main script
75 | ├── README.md                    # Project overview
76 | └── Crime_Data_from_2020_to_Present.csv   # Dataset (not included here)
77 | 🧠 Future Improvements
78 | Add support for filtering by date range or area.
79 | 
80 | Export summary statistics to CSV.
81 | 
82 | Integrate with a simple GUI or web interface (e.g., Streamlit).
83 | 


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/main.py:
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  1 | import numpy as np
  2 | import pandas as pd
  3 | import seaborn as sns
  4 | import matplotlib.pyplot as plt
  5 | 
  6 | # ---------------------- Load Dataset ------------------------
  7 | DATA_PATH = "C:\\Users\\hp\\Downloads\\Crime_Data_from_2020_to_Present.csv"  # Replace with actual path if needed
  8 | 
  9 | print("Loading CSV file...")
 10 | df = pd.read_csv(DATA_PATH)
 11 | print(f"Successfully loaded data with {df.shape[0]} rows and {df.shape[1]} columns")
 12 | 
 13 | # ---------------------- Basic Overview ----------------------
 14 | print("\nBasic Info:")
 15 | print(df.info())
 16 | 
 17 | print("\nFirst 5 Rows:")
 18 | print(df.head())
 19 | 
 20 | print("\nMissing Values:")
 21 | print(df.isnull().sum()[df.isnull().sum() > 0])
 22 | 
 23 | # ---------------------- Data Cleaning -----------------------
 24 | print("\nCleaning Data...")
 25 | df_cleaned = df.dropna(subset=["Date Rptd", "TIME OCC", "Crm Cd Desc", "AREA NAME"])
 26 | df_cleaned["Date Rptd"] = pd.to_datetime(df_cleaned["Date Rptd"], errors="coerce")
 27 | df_cleaned["TIME OCC"] = df_cleaned["TIME OCC"].astype(str).str.zfill(4)
 28 | 
 29 | # ---------------------- Set Plot Style -----------------------
 30 | sns.set(style="whitegrid")
 31 | plt.rcParams["figure.figsize"] = (12, 6)
 32 | 
 33 | # ---------------------- Visualization Functions -----------------------
 34 | 
 35 | def plot_top_crimes():
 36 |     print("\nTop 10 Reported Crimes...")
 37 |     top_crimes = df_cleaned["Crm Cd Desc"].value_counts().head(10)
 38 |     sns.barplot(x=top_crimes.values, y=top_crimes.index, color="salmon")
 39 |     plt.title("Top 10 Most Reported Crimes")
 40 |     plt.xlabel("Number of Reports")
 41 |     plt.ylabel("Crime Type")
 42 |     plt.show()
 43 | 
 44 | def plot_crimes_over_time():
 45 |     print("\nCrime Trend Over Time...")
 46 |     crime_trend = df_cleaned.groupby(df_cleaned["Date Rptd"].dt.to_period("M")).size()
 47 |     crime_trend.index = crime_trend.index.to_timestamp()
 48 |     plt.plot(crime_trend.index, crime_trend.values, color="steelblue", marker="o")
 49 |     plt.title("Monthly Crime Trend")
 50 |     plt.ylabel("Crime Count")
 51 |     plt.xlabel("Date")
 52 |     plt.xticks(rotation=45)
 53 |     plt.tight_layout()
 54 |     plt.show()
 55 | 
 56 | def plot_crimes_by_area():
 57 |     print("\nCrime Count by Area...")
 58 |     area_counts = df_cleaned["AREA NAME"].value_counts().head(10)
 59 |     sns.barplot(x=area_counts.values, y=area_counts.index, color="skyblue")
 60 |     plt.title("Top 10 Crime-Prone Areas")
 61 |     plt.xlabel("Number of Crimes")
 62 |     plt.ylabel("Area")
 63 |     plt.show()
 64 | 
 65 | def plot_hourly_distribution():
 66 |     print("\nCrimes by Hour of Day...")
 67 |     df_cleaned["Hour"] = df_cleaned["TIME OCC"].str[:2].astype(int)
 68 |     hourly = df_cleaned["Hour"].value_counts().sort_index()
 69 |     plt.plot(hourly.index, hourly.values, color="green", marker="o")
 70 |     plt.xticks(range(0, 24))
 71 |     plt.title("Crimes by Hour of Day")
 72 |     plt.xlabel("Hour")
 73 |     plt.ylabel("Crime Count")
 74 |     plt.grid(True)
 75 |     plt.show()
 76 | 
 77 | def plot_heatmap_area_vs_crime():
 78 |     print("\nHeatmap of Area vs Crime Type...")
 79 |     
 80 |     # Get top 10 areas and top 15 crime types
 81 |     top_areas = df_cleaned["AREA NAME"].value_counts().head(10).index
 82 |     top_crimes = df_cleaned["Crm Cd Desc"].value_counts().head(15).index
 83 | 
 84 |     df_filtered = df_cleaned[df_cleaned["AREA NAME"].isin(top_areas) & df_cleaned["Crm Cd Desc"].isin(top_crimes)]
 85 | 
 86 |     pivot = df_filtered.pivot_table(index="AREA NAME", columns="Crm Cd Desc", aggfunc="size", fill_value=0)
 87 | 
 88 |     print("Pivot shape:", pivot.shape)  # Debug print to confirm data is present
 89 | 
 90 |     sns.heatmap(pivot, cmap="coolwarm", linewidths=0.5, annot=True, fmt="d")
 91 |     plt.title("Crime Types by Area (Heatmap)")
 92 |     plt.xlabel("Crime Type")
 93 |     plt.ylabel("Area Name")
 94 |     plt.xticks(rotation=45, ha='right')
 95 |     plt.tight_layout()
 96 |     plt.show()
 97 | 
 98 | # ------------------ Interactive Menu -----------------------
 99 | 
100 | def run_interactive_analysis():
101 |     while True:
102 |         print("\n" + "="*50)
103 |         print("LAPD CRIME DATA ANALYSIS MENU")
104 |         print("="*50)
105 |         print("1. Basic Statistics")
106 |         print("2. Top Crimes")
107 |         print("3. Crime Trend Over Time")
108 |         print("4. Crimes by Area")
109 |         print("5. Hourly Crime Distribution")
110 |         print("6. Area vs Crime Heatmap")
111 |         print("7. Run All")
112 |         print("0. Exit")
113 | 
114 |         choice = input("\nEnter your choice (0-7): ")
115 |         if choice == '1':
116 |             print(df_cleaned.describe(include='all'))
117 |         elif choice == '2':
118 |             plot_top_crimes()
119 |         elif choice == '3':
120 |             plot_crimes_over_time()
121 |         elif choice == '4':
122 |             plot_crimes_by_area()
123 |         elif choice == '5':
124 |             plot_hourly_distribution()
125 |         elif choice == '6':
126 |             plot_heatmap_area_vs_crime()
127 |         elif choice == '7':
128 |             plot_top_crimes()
129 |             plot_crimes_over_time()
130 |             plot_crimes_by_area()
131 |             plot_hourly_distribution()
132 |             plot_heatmap_area_vs_crime()
133 |         elif choice == '0':
134 |             print("\nThank you for using the LAPD Crime Data Analysis Tool!")
135 |             break
136 |         else:
137 |             print("Invalid choice. Try again!")
138 | 
139 | # ---------------------- MAIN ----------------------------
140 | 
141 | run_interactive = input("\nDo you want to run interactive analysis? (y/n): ")
142 | if run_interactive.lower() == 'y':
143 |     run_interactive_analysis()
144 | else:
145 |     print("You Entered Wrong Input , Now You Have To Face So Many Graphs And All")
146 |     plot_top_crimes()
147 |     plot_crimes_over_time()
148 |     plot_crimes_by_area()
149 |     plot_hourly_distribution()
150 |     plot_heatmap_area_vs_crime()
151 |     print("\nAll analysis complete!")
152 | 


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