Predicting Sale Prices Using Random Forest Regression

Feb 25, 2025, 9:13 PM

recent_sales_attributes_unlimited_inputs_only.csv

Missing file

I would like to predict sale price based on all the other attributes in this file by comparing the most popular models

To predict the sale price based on the other attributes, we can use a machine learning model. A common approach is to use a regression model since the target variable, sale_price_dollars, is continuous.
Here's a step-by-step plan:
  1. Data Preprocessing: Convert categorical variables into numerical ones using techniques like one-hot encoding.
  2. Feature Selection: Identify the most relevant features for predicting the sale price.
  3. Model Selection: Choose a regression model, such as Linear Regression, Decision Tree, or Random Forest.
  4. Model Training: Train the model using the dataset.
  5. Model Evaluation: Evaluate the model's performance using metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).
Let's start by preprocessing the data and preparing it for modeling.
The Random Forest Regression model has been trained to predict the sale_price_dollars based on the other attributes in the dataset. The model's performance was evaluated using the Mean Absolute Error (MAE), which is approximately 4.15.
This means that, on average, the model's predictions are off by about $4.15 from the actual sale prices.
If you have any further questions or need additional analysis, feel free to ask!

Can you show a plot of the predicted vs actual sale prices?

Can you try a different regression model?

Let's try using a different regression model to see if we can improve the prediction accuracy. We'll use a Gradient Boosting Regressor, which is another popular ensemble method for regression tasks.
I'll train the model and evaluate its performance using the same dataset.
The Gradient Boosting Regressor model has been trained and evaluated. The Mean Absolute Error (MAE) for this model is approximately 4.15, which is quite similar to the Random Forest model.
This suggests that both models perform similarly on this dataset in terms of prediction accuracy.
If you have any further questions or need additional analysis, feel free to ask!

Which is easier/faster to compute?

The computational efficiency of a model depends on various factors, including the complexity of the algorithm, the size of the dataset, and the number of features.

Random Forest vs. Gradient Boosting:

  • Random Forest:
    • Easier to Parallelize: Each tree in the forest is built independently, making it easier to parallelize.
    • Faster Training: Generally faster to train than Gradient Boosting, especially with a large number of trees.
    • Less Sensitive to Hyperparameters: Requires less tuning compared to Gradient Boosting.
  • Gradient Boosting:
    • Sequential Training: Trees are built sequentially, which can make it slower to train.
    • More Sensitive to Hyperparameters: Often requires careful tuning of learning rate, number of trees, etc.
    • Potentially Better Performance: Can achieve better performance with the right tuning and is often preferred for competitions.
In summary, Random Forest is generally faster and easier to compute, especially for larger datasets or when computational resources are limited. However, Gradient Boosting can provide better performance with the right tuning, albeit at the cost of increased computational time.
If you have any more questions or need further clarification, feel free to ask!

Can you compare these results to the other traditional machine learning models?

To compare the results of Random Forest and Gradient Boosting with other traditional machine learning models, we can consider models like:
  1. Linear Regression
  2. Decision Tree Regressor
  3. Support Vector Regressor (SVR)
Let's train and evaluate these models on the same dataset to see how they perform in comparison.
Here are the Mean Absolute Errors (MAE) for the different models:
  1. Linear Regression: MAE = 4.08
  2. Decision Tree Regressor: MAE = 4.58
  3. Support Vector Regressor (SVR): MAE = 4.07

Comparison:

  • Support Vector Regressor (SVR) and Linear Regression have the lowest MAE, indicating better performance compared to the other models.
  • Decision Tree Regressor has the highest MAE, suggesting it may not be the best choice for this dataset.
Overall, SVR and Linear Regression perform slightly better than Random Forest and Gradient Boosting in terms of MAE.
If you have any further questions or need additional analysis, feel free to ask!

Can you show me the relative importance of all features?

To determine the relative importance of features, we can use models that provide feature importance metrics, such as Random Forest or Gradient Boosting. These models can give us insights into which features are contributing the most to the predictions.
Let's calculate and visualize the feature importances using the Random Forest model.
The plot above shows the feature importances as determined by the Random Forest model. The features are sorted in descending order of importance, indicating which features contribute the most to the model's predictions.
  • Features with higher bars are more important in predicting the target variable.
  • This visualization helps in understanding which features are driving the model's decisions.
If you have any further questions or need additional analysis, feel free to ask!

Can you exclude brand from this graph?

Can you summarize this based on type of attribute?

The plot above summarizes the feature importances based on the type of attribute:
  • Color: Represents the average importance of all color-related features.
  • Size: Represents the average importance of all size-related features.
  • Quantity: Represents the average importance of the quantity feature.
  • Price: Represents the average importance of price-related features.
  • Other: Represents the average importance of features that don't fall into the above categories.
This visualization helps in understanding which types of attributes are more influential in the model's predictions.
If you have any further questions or need additional analysis, feel free to ask!

Can you show a correlation matrix of the features?

The heatmap above displays the correlation matrix of the features after applying one-hot encoding to categorical variables. This matrix shows the pairwise correlation coefficients between features, which can range from -1 to 1:
  • 1 indicates a perfect positive correlation.
  • -1 indicates a perfect negative correlation.
  • 0 indicates no correlation.
This visualization helps in identifying any multicollinearity among features, which can be useful for feature selection and understanding relationships between variables.
If you have any further questions or need additional analysis, feel free to ask!

Can you identify any highly correlated features?

There are no pairs of features with a correlation coefficient greater than 0.8 or less than -0.8 in the dataset. This indicates that there are no highly correlated features among the encoded features, suggesting minimal multicollinearity.
If you have any further questions or need additional analysis, feel free to ask!

What is the distribution of sale prices?

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