The Setup and the Crash #
I built this as a real-world exercise in prompt engineering for data cleaning and basic ML deployment. My goal was to see if I could predict a film's score based on metadata. I used a standard scikit-learn pipeline, but I hit a wall during the preprocessing stage.
The first issue was a classic ValueError
when I tried to fit the Linear Regression model. I hadn't handled the categorical variables (like Genre) correctly, and the model choked on the strings.
ValueError: could not convert string to float: 'Action'
I fixed this using one-hot encoding, but then I ran into a performance bug. My dataset had a few extreme outliers—movies with massive budgets but 1-star ratings—and the Linear Regression model was being pulled wildly off course by them.
Comparing the Results #
Since I wanted a deep dive into why one worked better than the other, I tracked a few specific metrics. I can't use a table here, so here is the breakdown of how they performed on the test set:
Linear Regression MAE: 1.42 (way too high for a 1-10 scale)Random Forest MAE: 0.68 (much closer to the actual scores)Linear Regression R² Score: 0.31Random Forest R² Score: 0.74
The Random Forest model won because it handles non-linear relationships and interactions between features much better. For example, the interaction between "Director Reputation" and "Genre" is complex; a horror movie might be rated highly for being "scary," whereas a drama is rated for "acting." Linear Regression tries to find a global average, whereas the decision trees in Random Forest can isolate these specific pockets of data.
My Practical Tutorial for Implementation #
If you're trying to replicate this or doing a similar LLM agent project for data analysis, here is the basic logic I used for the Random Forest implementation:
-
Load the IMDb dataset and drop rows with missing values.
-
Encode categorical features using
pd.get_dummies()
.
- Split the data 80/20 using
train_test_split
.
- Initialize the
RandomForestRegressor
with n_estimators=100
and max_depth=10
to prevent overfitting.
- Fit the model and evaluate using
mean_absolute_error
.
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
predictions = rf_model.predict(X_test)
print(f"MAE: {mean_absolute_error(y_test, predictions)}")
It's a good reminder that "simpler" isn't always "better" if the underlying data distribution is chaotic.
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