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import numpy as np | ||
import pandas as pd | ||
from sklearn.model_selection import train_test_split | ||
from sklearn.linear_model import LinearRegression | ||
from sklearn.metrics import mean_squared_error, r2_score | ||
import joblib | ||
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class PredictiveAnalytics: | ||
def __init__(self): | ||
""" | ||
Initialize the PredictiveAnalytics class. | ||
""" | ||
self.model = LinearRegression() | ||
self.is_trained = False | ||
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def train(self, data, target_column): | ||
""" | ||
Train the predictive model using the provided data. | ||
:param data: DataFrame containing the features for training. | ||
:param target_column: The name of the target column to predict. | ||
""" | ||
X = data.drop(columns=[target_column]) | ||
y = data[target_column] | ||
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# Split the data into training and testing sets | ||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | ||
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# Train the model | ||
self.model.fit(X_train, y_train) | ||
self.is_trained = True | ||
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# Evaluate the model | ||
predictions = self.model.predict(X_test) | ||
mse = mean_squared_error(y_test, predictions) | ||
r2 = r2_score(y_test, predictions) | ||
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print(f"Model trained successfully. MSE: {mse}, R^2: {r2}") | ||
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def predict(self, new_data): | ||
""" | ||
Make predictions using the trained model. | ||
:param new_data: DataFrame containing the features for prediction. | ||
:return: Array of predictions. | ||
""" | ||
if not self.is_trained: | ||
raise Exception("Model is not trained yet. Please train the model before making predictions.") | ||
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return self.model.predict(new_data) | ||
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def save_model(self, filename): | ||
""" | ||
Save the trained model to a file. | ||
:param filename: The name of the file to save the model. | ||
""" | ||
joblib.dump(self.model, filename) | ||
print(f"Model saved to {filename}") | ||
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def load_model(self, filename): | ||
""" | ||
Load a trained model from a file. | ||
:param filename: The name of the file to load the model from. | ||
""" | ||
self.model = joblib.load(filename) | ||
self.is_trained = True | ||
print(f"Model loaded from {filename}") | ||
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# Example usage | ||
if __name__ == "__main__": | ||
# Sample data for demonstration | ||
data = pd.DataFrame({ | ||
'feature1': [1, 2, 3, 4, 5], | ||
'feature2': [2, 3, 4, 5, 6], | ||
'target': [3, 5, 7, 9, 11] | ||
}) | ||
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# Initialize the predictive analytics model | ||
analytics = PredictiveAnalytics() | ||
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# Train the model | ||
analytics.train(data, target_column='target') | ||
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# Make predictions | ||
new_data = pd.DataFrame({ | ||
'feature1': [6, 7], | ||
'feature2': [7, 8] | ||
}) | ||
predictions = analytics.predict(new_data) | ||
print("Predictions:", predictions) | ||
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# Save the model | ||
analytics.save_model("predictive_model.pkl") | ||
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# Load the model | ||
analytics.load_model("predictive_model.pkl") |