Jupyter Notebook that analyzes India's used car market providing various insights.
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Understand the overall structure of the Indian used-car market in the dataset
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Identify which features (e.g., year, kilometers driven, fuel type, transmission, brand/model) most influence price
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Detect outliers and data quality issues that affect analysis
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Produce visual insights that can inform buyers/sellers and future predictive modeling
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How does price vary by brand and model?
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What is the relationship between kilometers driven and price?
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How do fuel type and transmission affect resale price?
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Do newer model years show significantly higher resale value?
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Which segments show the best “value” (price vs. age vs. usage)?
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Are there extreme outliers (unrealistic year/km/price) that should be removed?
- Python
- Jupyter Notebook
- Libraries: pandas, numpy, matplotlib, seaborn