Certainly! Here's an example of a simple machine learning prediction model using Python and scikit-learn library for linear regression:

```python
# Import required libraries
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

# Load and preprocess the data
# Assuming you have a CSV file named "data.csv" with features (X) and target (y) columns
df = pd.read_csv("data.csv")
X = df.iloc[:, :-1]  # Features
y = df.iloc[:, -1]   # Target

# 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)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict on test data
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
```

Note: This is just a basic example of a linear regression model. In a real-world scenario, you would need to preprocess and clean the data, handle missing values, scale the features, and tune the model parameters to optimize performance. Additionally, depending on the problem you're trying to solve, you may need to use other types of machine learning models, such as decision trees, support vector machines, or neural networks. The code provided serves as a starting point and can be expanded upon depending on the specific requirements of your project.
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