Used Car Price Dataset

🚗 Used Car Price Dataset: A dataset for predicting used car price 📊

Dive into the world of used cars with our dataset, perfect for predicting prices. It's a carefully selected set of data that car enthusiasts, analysts, and data scientists will find valuable. Whether you're curious or looking to analyze, this dataset is your guide to understanding the dynamics of how used cars are valued.

Key Features:

• 🛣️ Rich Attributes: Explore a number of attributes, including mileage, model year, fuel type, transmission, and more, providing a 360-degree view of each vehicle's specifications.
• 📉 Depreciation Insights: Uncover patterns in vehicle depreciation over time and across different makes and models, empowering you to make informed predictions about future price trends.
• 📱 Technological Integration: Seamlessly integrate our dataset into your predictive modeling pipelines, harnessing the power of technology to foresee changes in the used car market.

Potential Applications:

• 📈 Market Research: Conduct in-depth market research to identify trends, fluctuations, and hotspots in the used car industry.
• 🤖 Predictive Modeling: Build robust machine learning models to predict resale values, assisting buyers, sellers, and dealerships in making informed decisions.
• 🚀 Business Strategy: Inform business strategies for used car dealerships, insurance companies, and financial institutions by understanding the underlying factors influencing pricing.

How to Use:

1. 🧑‍💻 Data Science Projects: Integrate this dataset into your data science projects to explore and analyze factors impacting used car prices.
2. 🚀 Predictive Modeling: Train machine learning models to predict resale values based on historical data and a wide array of vehicle attributes.
3. 🚗 Market Insights: Gain valuable insights into market dynamics, allowing you to stay ahead of trends and developments in the used car space.

Dataset Description:

1552 Rows, 15 Columns

Attributes:

1. car_name
2. registration_year
3. insurance_validity
4. fuel_type
5. seats
6. kms_driven
7. ownership
8. transmission
9. manufacturing_year
10. mileage(kmpl)
11. engine(cc)
12. max_power(bhp)
13. torque(Nm)
14. price(in lakhs)

Steps you can do:

1. Data Preprocessing
2. Data Visualization
3. Explarotary Data Analysis
4. Feature Selection and Transformation
5. Train-Test-Split
6. Model Creation (eg: Multiple Linear Regression)
7. Model Prediction

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