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Sklearn.linear model.LinearRegression

ollama에서 llama 3 의 답변

질문 내용: 

중고차의 "연비, 연비, 정비횟수, 연비, 연식" 제공 시 Python을 활용한 추천 시스템을 구현합니다.

영문 프롬프트: 

Implement a recommendation system using Python when providing "mileage, fuel consumption, number of maintenance, fuel efficiency, and model year" of a used vehicle.

라마 실행: 

$ ollama run llama3-instruct-8b

Here is a simple implementation of a recommendation system in Python that uses the provided features (mileage, fuel consumption, number of maintenance, fuel efficiency, and model year) to recommend vehicles based on user preferences. 

import pandas as pd

# Load the dataset
df = pd.read_csv('vehicle_data.csv')

# Define the features and target variable
X = df[['mileage', 'fuel_consumption', 'number_of_maintenance', 'fuel_efficiency', 'model_year']]
y = df['target_variable']  # Assuming the target variable is a categorical column

# Split the data into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a model (e.g., linear regression)
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = model.predict(X_test)

# Evaluate the model's performance
from sklearn.metrics import accuracy_score
print("Model Accuracy:", accuracy_score(y_test, y_pred))

# Define a function to recommend vehicles based on user preferences
def recommend_vehicles(user_preferences):
    # Calculate the distance between the user's preferences and each vehicle in the training set
    distances = []
    for i in range(len(X_train)):
        distance = 0
        for j in range(len(user_preferences)):
            distance += abs(user_preferences[j] - X_train.iloc[i][j])
        distances.append(distance)

    # Get the indices of the top N vehicles with the smallest distances
    top_N_indices = sorted(range(len(distances)), key=lambda i: distances[i])[:N]

    # Return the recommended vehicles
    return [y_train.iloc[i] for i in top_N_indices]

# Example usage:
user_preferences = [10000, 10, 2, 30, 2015]  # Replace with user's preferences
recommended_vehicles = recommend_vehicles(user_preferences)
print("Recommended Vehicles:", recommended_vehicles)

This is a basic example and may not work well for your specific use case. You might need to preprocess the data, tune hyperparameters, or try different models to improve the performance of the recommendation system.

Also, you'll need to replace vehicle_data.csv with the actual path to your dataset file, and target_variable with the name of the target variable in your dataset.

Remember that this is a simple example and real-world recommendation systems are much more complex. They often involve more advanced techniques such as collaborative filtering, matrix factorization, or deep learning-based models.

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