This project focuses on analyzing a dataset of used cars and building a simple predictive model to estimate selling prices based on various car features.
The goal is to understand feature relationships, clean and preprocess the data, perform visual exploration, and implement a Linear Regression model to predict car prices.
Car_Price_Analysis_Abderrahim.ipynb: Main Jupyter Notebook containing EDA, preprocessing, modeling, and evaluationcars.csv: Dataset (not included here for licensing reasons)README.md: Project documentation (this file)
- Explore key attributes affecting car prices (e.g. mileage, fuel type, power)
- Detect and handle missing values
- Engineer features and encode categorical data
- Visualize relationships and correlations
- Train and evaluate a Linear Regression model
- Predict the price of a new car
- Selling price is highly correlated with
max_powerandengine - Cars with more previous owners tend to sell for less
- Outliers are present in price, mileage, and power — identified via boxplots
- Categorical variables such as
fuel,transmission, andownersignificantly influence pricing
- Languages: Python (Pandas, NumPy, Seaborn, Matplotlib)
- Modeling: Scikit-learn (Linear Regression)
- Notebook: Jupyter (.ipynb)
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Clone this repository:
git clone https://github.com/your-username/car-price-analysis.git cd car-price-analysis -
Install dependencies (optional):
pip install pandas numpy matplotlib seaborn scikit-learn
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Launch the notebook:
jupyter notebook Car_Price_Analysis_Abderrahim.ipynb
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Replace or add your own
cars.csvdataset file in the same directory.
The notebook ends with a sample prediction for a new car using this input format:
new_car = [[73000, 0, 0, 0, 1, 45, 2775, 86, 2, 9]]This array represents a car’s numerical features (e.g. mileage, fuel, transmission, power, age...).
Abderrahim Jridi
LinkedIn
Email: abderrahim.jridi@gmail.com
“Data is the new oil, but only if refined.”
Let’s build better decisions with clean, structured, and intelligent data.