🏡 House Price Prediction

For this problem you need to implement a regression model capable of predicting the value of a house price (Price) using an available dataset.
The model's performance will be evaluated based on the mean absolute error (MAE).

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📊 Dataset

The dataset is organized in a CSV file and contains the following columns:

• Square_Footage: House area
• Num_Bedrooms: Number of bedrooms
• Num_Bathrooms: Number of bathrooms
• Year_Built: Year of construction
• Lot_Size: Lot size
• Garage_Size: Garage size
• Neighborhood_Quality: Neighborhood quality
• Footage_to_Lot_Ratio: Ratio between house area and lot size
• Total_Rooms: Total number of rooms
• Age_of_House: Age of the house
• Garage_to_Footage_Ratio: Ratio between garage size and house area
• Avg_Room_Size: Average room size
• Price: Target variable, house price (numerical value)
• House_Orientation_Angle: House orientation angle
• Street_Alignment_Offset: Street alignment
• Solar_Exposure_Index: Solar exposure index
• Magnetic_Field_Strength: Magnetic field strength
• Vibration_Level: Vibration level

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🧩 Tasks

🔹 Subtask 1 (10p)

For each house in the test set, determine the estimated total area as the sum of:
Square_Footage + Garage_Size + Lot_Size.

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🔹 Subtask 2 (10p)

For each house in the test set, calculate the ratio:
Garage_Size / Total_Rooms
The result will be added as a new column: Garage_to_Room_Ratio.

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🔹 Subtask 3 (10p)

Calculate the environmental stability index:

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🔹 Subtask 4 (10p)

1. Calculate the mean of the Square_Footage column on the training set.
2. For each house in the test set, determine:
   |Square_Footage - Mean(Square_Footage_train)|

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🔹 Subtask 5 (60p)

Build a regression model for predicting the Price field, using the training set dataset_train.csv.
Determine the predictions on the evaluation set dataset_eval.csv (which does not contain the Price column).

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🧠 Notes about the dataset

• Target field: Price
• Numerical variables can be used directly for regression.
• It is recommended to handle missing values and apply scaling/normalization to improve model performance.

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🧮 Evaluation criteria

• Performance: The model must achieve the lowest possible MAE.

Formula used for MAE calculation:

where:

• N = number of examples in the test set,
• y_i = actual value,
• y^_i = value predicted by the model.

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💡 Note

If you submit sample_output, you will receive 5 points.

🗂️ Useful resources

• Complete Starter Kit – contains a skeleton from which you can start solving the problem