├── PYTHON PROJECT REPORT.pdf ├── README.md ├── Screenshot (129).png ├── Screenshot (130).png ├── Screenshot (131).png ├── Screenshot (132).png ├── Screenshot (133).png └── code.py /PYTHON PROJECT REPORT.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/PYTHON PROJECT REPORT.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Insights Derived from Stolen Vehicle Datasets through Data Analysis Using Python. 2 | -------------------------------------------------------------------------------- /Screenshot (129).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/Screenshot (129).png -------------------------------------------------------------------------------- /Screenshot (130).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/Screenshot (130).png -------------------------------------------------------------------------------- /Screenshot (131).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/Screenshot (131).png -------------------------------------------------------------------------------- /Screenshot (132).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/Screenshot (132).png -------------------------------------------------------------------------------- /Screenshot (133).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/deep497/Vehicle-Theft-Data-Analysis/5ea244642efe5ef644455cc0669a225faa6d68c9/Screenshot (133).png -------------------------------------------------------------------------------- /code.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | import seaborn as sns 4 | 5 | df = pd.read_csv("clean_stolen_vehicless.csv") 6 | df.dropna(inplace=True) 7 | df['date_stolen'] = pd.to_datetime(df['date_stolen'], errors='coerce') 8 | 9 | sns.set(style="whitegrid") 10 | plt.rcParams["figure.figsize"] = (10, 6) 11 | 12 | plt.figure() 13 | sns.heatmap(df.isnull(), cbar=False, cmap='viridis') 14 | plt.title('Missing Data Heatmap') 15 | plt.tight_layout() 16 | 17 | plt.figure() 18 | sns.countplot(data=df, x='vehicle_type', order=df['vehicle_type'].value_counts().index) 19 | plt.xticks(rotation=45) 20 | plt.title('Vehicle Type Distribution') 21 | plt.tight_layout() 22 | 23 | plt.figure() 24 | sns.countplot(data=df, x='color', order=df['color'].value_counts().index) 25 | plt.xticks(rotation=45) 26 | plt.title('Vehicle Color Distribution') 27 | plt.tight_layout() 28 | 29 | plt.figure() 30 | sns.histplot(df['model_year'], bins=30, kde=True) 31 | plt.title('Distribution of Model Years') 32 | plt.tight_layout() 33 | 34 | df['year_month'] = df['date_stolen'].dt.to_period('M') 35 | monthly_counts = df['year_month'].value_counts().sort_index() 36 | 37 | plt.figure() 38 | monthly_counts.plot(kind='line', marker='o') 39 | plt.title('Number of Vehicles Stolen Over Time') 40 | plt.xlabel('Year-Month') 41 | plt.ylabel('Count') 42 | plt.xticks(rotation=45) 43 | plt.tight_layout() 44 | 45 | plt.figure() 46 | sns.heatmap(df.select_dtypes(include='number').corr(), annot=True, cmap='coolwarm') 47 | plt.title('Correlation Heatmap') 48 | plt.tight_layout() 49 | 50 | plt.show() 51 | --------------------------------------------------------------------------------