Since I bought a laptop, I wanted to check if I got scammed or not by applying my newly aquired machine learning knowledge. Given the specs of my laptop, how much should it have cost?

A Kaggle dataset about the characteristics and price for 1300 laptop models.

1. Data preperation
2. Building the model
3. Evaluation

Target column: Price_euros

Feature columns:

1. Drop columns that contain NA-Values
2. Columns with categorical data, cardinality < 10
3. Columns with numerical data

My laptop data: For the given columns, I looked up the specs of my laptop.

The tests have been done with a XGBRegressor-model since that is the most accurate one I learned so far.

1. Create a scoring method get_score to evaluate quality of the model based on Mean Average Error (MAE)
2. Run a loop that determines the best parameters (n_estimators, learning_rate) for this model.

• XGBRegressor
• n-estimators = 200
• learning_rate = 0.05
• Mean Average Error: 285.74 €

The model estimates the price of my laptop to be 1727.37€, but I paid barely 800€. This could indicate that the model is bad.

1. Better Data Cleaning:

   • Analyse if dataset actually contains reasonable prices.
   • Analyse which columns that might cause overfitting.
   • Include columns with NA-values.
   • Learn from other peoples Work.

2. Better Model Creation:

   • Learn about more models and how they work.
   • Find a better fitting model.
   • Adjust paramters for lower Mean Average Error.