This project is a deep exploratory data analysis (EDA) of the Indian bike market. We aimed to answer key questions about pricing trends, mileage impact, and resale value predictions using statistical modeling. The dataset contains various attributes related to motorcycle sales, including brand, owner type, mileage, and price.
The dataset includes:
- Brand: The manufacturer of the bike (e.g., Bajaj, Hero, Honda, etc.)
- Model: The specific model of the bike
- Year: Year of manufacturing
- Owner Type: First-hand, second-hand, etc.
- Mileage (km/l): Fuel efficiency of the bike
- Price (INR): Original and resale price data
- What are the top-selling bike brands in the resale market?
- How does mileage affect the resale price?
- Does the number of previous owners impact the price significantly?
- Which brand retains the highest resale value over time?
- How do pricing trends vary based on mileage and owner type?
- Data Cleaning & Preprocessing
- Handled missing values, outliers, and inconsistencies
- Exploratory Data Analysis (EDA)
- Visualized brand-wise pricing trends and mileage distributions
- Statistical Modeling for Resale Price Prediction
- Built a linear regression model to predict resale value based on price and mileage
- Mileage has a significant impact on resale value, but the effect varies by brand.
- Second-hand bikes show a high variance in pricing depending on their condition.
- Certain brands, like Honda and Royal Enfield, have better resale value retention.
- Enhancing the model with more features (e.g., service history, accident records)
- Implementing machine learning models for more accurate predictions
- Expanding the dataset with real-time data collection
This project provides a comprehensive analysis of India's bike market and can assist both buyers and sellers in making informed decisions. 🚴♂️💨
Feel free to explore the analysis and share your feedback! 😊