PredictorPro

Getting Started with PredictorPro

1. Install PredictorPro

Make sure you have PredictorPro installed. You can typically do this via pip:

pip install predictorpro

2. Import Libraries

Start by importing the necessary libraries:

import predictorpro as pp
import pandas as pd

3. Load Your Data

You can load your dataset using pandas. For this example, let’s say you have a CSV file.

data = pd.read_csv('your_dataset.csv')

4. Preprocess Your Data

Make sure your data is clean and prepared for modeling. This might include handling missing values, encoding categorical variables, etc.

# Example of filling missing values
data.fillna(method='ffill', inplace=True)

# Example of encoding categorical variables
data = pd.get_dummies(data, drop_first=True)

5. Split Your Data

You’ll want to split your data into features and the target variable, then into training and testing sets.

from sklearn.model_selection import train_test_split

X = data.drop('target_column', axis=1)
y = data['target_column']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

6. Create a PredictorPro Model

Now, you can create and train your PredictorPro model.

model = pp.Predictor()

# Fit the model
model.fit(X_train, y_train)

7. Make Predictions

Once the model is trained, you can make predictions on the test set.

predictions = model.predict(X_test)

8. Evaluate the Model

You can evaluate the performance of your model using various metrics.

from sklearn.metrics import accuracy_score, classification_report

accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy}')

print(classification_report(y_test, predictions))