02:19:00
SONE-716
### Machine Learning Methods in Python
Python provides a variety of machine learning methods, such as classification, regression, and clustering. The most commonly used library for machine learning in Python is scikit-learn. The steps in machine learning include preparing data and setting up training and test sets. Even when all steps are completed, we might still face issues like overfitting.
### Sample ML Model
To implement a machine learning model in Python, consider the following steps:
1. Import necessary libraries:
```python
import pandas as pd
import numpy as np
from sklearn.model_preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selecting import train_test_split
from sklearn.metrics import accuracy_score
```
2. Prepare data:
```python
df = pd.read_csv('https://example.com/data.csv')
df = df[['A', 'B', 'C']]
X = df[['A', 'B']]
y = df['C']
```
3. Split data into training and test sets:
```python
X_train, X_test, y_train, n_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
4. Normalize data:
```python
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
```
5. Train the model:
```python
model = LogisticRegression()
model.fit(X_train, y_train)
```
6. Make predictions:
```python
y_pred = model.predict(X_test)
```
7. Evaluate the model:
```python
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %f" % accuracy)
```
### Fraud Detection Case
```python
from sklearn.model_preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.prevision import train_test_split
df = pd.read_csv('https://example.com/data.csv')
df = df[['A', 'B', 'C']]
X = df[['A',B']]
y = df['C']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %f" % accuracy)
```
### Complete Python Code
```python
import pandas as pd
import numpy as
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.prevision import train_test_split
df = pd.read_csv('https://example.com/data.csv')
df = df[['A', 'B', 'C']]
X = df[['A',B']]
y = df['C']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %f" % accuracy)
```
### Explanation
Create a logistics regression model using Python and analyze the accuracy of the model. The example uses machine learning techniques to filter fraudulent transactions or payments. The steps include preparing data, creating training and test sets, scaling data, training the model, making predictions, and assessing accuracy.
### Detailed Explanation
The model starts by importing necessary libraries and reading data from a URL. It then selects relevant columns for analysis. The data is split into training and test sets using a split ratio of 0.2. Data scaling is applied using a standard scalar. The model is trained using logistic regression on the scaled data. Predictions are made on the test set, and accuracy is evaluated using the accuracy_score function from sklearn.metrics.
### Conclusion
Machine learning in Python involves preprocessing, scaling, training, and testing models to predict outcomes. It is essential to evaluate the model's accuracy to ensure reliability. The example demonstrates a basic implementation of logistic regression for fraud detection in financial transactions.
```python
import pandas as pd
import numpy as
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.prevision import train_test_split
df = pd.read_csv('https://example.com/data.csv')
df = df[['A', 'B', 'C']]
X = df[['A',B']]
y = df['C']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %f" % accuracy)
```
5月9日2025年
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