This project is part of the "Supervised Learning: Regression" course and involves developing a predictive model for determining the prices of used cars. Utilizing the Cars4U dataset, we aim to create a model that accurately forecasts used car prices, assisting in the formulation of profitable strategies through differential pricing.
The primary objective of the Cars4U project is to devise a reliable pricing model based on various influencing factors captured in the dataset. This model is expected to guide decision-making processes in pricing used cars and unveiling new business opportunities through strategic pricing.
- Data Exploration: Perform an in-depth analysis of the dataset, identifying key metrics, trends, and relationships that could influence used car prices.
- Predictive Modeling: Build a robust regression model that can accurately predict the prices of used cars based on historical data.
- Strategy Formulation: Leverage the insights gained from the model to propose differential pricing strategies that could enhance business profitability.
- Familiarity with regression models and predictive analytics.
- Required software: Python, Jupyter Notebook, or any preferred IDE supporting Python.
- Required libraries: scikit-learn, pandas, NumPy, matplotlib, seaborn.