This project analyzes the Ontario vehicle population dataset to uncover insights into optimal car choices based on various factors such as manufacturing year, manufacturer, model, number of cylinders, and body type. The dataset includes information on fit cars, wrecked cars, and other statistics for different vehicle models.
- Description
- Dataset Information
- Imports
- Data Loading
- Optimal Manufacturing Year Range
- Most Durable Manufacturers
- Popular and Reliable Car Models
- Optimal Car Features
- Conclusion
This project aims to provide insights into the Ontario automotive landscape by analyzing data on vehicle models, manufacturers, and various attributes such as manufacturing year, number of cylinders, and body type. Through visualizations and analysis, the project identifies optimal car choices for consumers based on reliability, popularity, and other factors.
- Descriptors.TXT: Contains information on vehicle types, including body type and number of cylinders.
- MakeAndModel.TXT: Contains data on specific vehicle models, including fit cars, wrecked cars, and other statistics.
- data_dictionary.xlsx: Provides an explanation of the dataset's contents and structure.
The project utilizes various Python libraries for data analysis and visualization, including pandas, matplotlib, plotly, and seaborn.
The dataset is loaded into the project using pandas, and preprocessing steps are applied to filter and clean the data for analysis.
An analysis of the relationship between a car's manufacturing year and its condition is performed to identify optimal manufacturing year ranges for purchasing used vehicles.
The project identifies the most reliable car manufacturers based on the proportion of active cars to total cars and the total count of active cars on the road.
Top car models from the most reliable manufacturers are identified based on the number of fit and active cars on the road.
A heatmap visualization is used to investigate the relationship between car features such as cylinder count and body type and the percentage of fit and active cars.
The project concludes by recommending optimal car choices based on the analysis results, including recommendations for specific manufacturers, models, and features.