Alright, so let me tell you about this “rome final” thing I tackled. It was a bit of a journey, not gonna lie.
First off, I started by setting up the environment. I figured, gotta have a clean workspace, right? So, I spun up a new virtual environment, installed all the necessary libraries. You know, the usual suspects like pandas, scikit-learn, matplotlib – the whole gang.
Then, I grabbed the data. It was a CSV file, a decent size one. Used pandas to read it in, peeked at the first few rows with .head(), just to get a feel for what I was dealing with. Saw a bunch of columns, some numerical, some categorical. Looked like a classic dataset ripe for some machine learning action.
Next, I dove into data cleaning. Oh boy, this took a while. Missing values everywhere! Decided to go with imputation – filled the numerical ones with the mean, and the categorical ones with the mode. Also, there were some weird outliers in one of the columns. I clipped them using a percentile-based approach. Basically, anything above the 95th percentile or below the 5th percentile got squashed down to those limits.
After cleaning, it was feature engineering time. This is where things got interesting. I created a few new features by combining existing ones. Tried some polynomial features too, just to see if they’d help. Then, I converted the categorical features into numerical ones using one-hot encoding. Ended up with a pretty wide dataset at this point.
Then came the model selection part. I tried a bunch of different models: Logistic Regression, Random Forest, Gradient Boosting, even threw in a Neural Network for good measure. Split the data into training and testing sets. Trained each model on the training data, and then evaluated them on the testing data. I was using accuracy as my primary metric. I also looked at precision and recall, just to make sure I wasn’t missing anything important.
The Random Forest performed the best, surprisingly. I say surprisingly because I thought the Neural Network would crush it, but nope. So, I decided to focus on tuning the Random Forest. I used GridSearchCV to find the best hyperparameters. It took a while to run, but it was worth it. Got a decent boost in performance.
Finally, I saved the model. Used pickle to serialize the trained Random Forest model. Also, I saved the scaler that I used for scaling the data. That way, I can load the model and scaler later and use them to make predictions on new data. Boom, done!
- Data Loading: Used pandas to load the CSV.
- Data Cleaning: Handled missing values and outliers.
- Feature Engineering: Created new features and encoded categorical ones.
- Model Selection: Trained and evaluated various models.
- Hyperparameter Tuning: Used GridSearchCV to optimize the Random Forest.
- Model Saving: Saved the trained model and scaler.
Overall, it was a fun project. Learned a lot about data cleaning, feature engineering, and model selection. Definitely a good experience!
