<s>[INST] ### Import the necessary libraries.

```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
```

### Define data set
```python
# Load the dataset
data_url = "https://raw.githubusercontent.com/elian-attias/my-Passion-Project/main/credit_data.csv"
credit_df = pd.read_csv(data_url)
credit_df.head()

# Define features (X) and target (y)
X = credit_df[['Age', 'Credit.Score', 'Years.In Fang.Exchanges..Currently']]
y = credit_df['Is.overdeadline']
```

### LDA (Linear Discriminate Analysis)
```python
# 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 an LDA instance
lda = LinearDiscriminantAnalysis()

# Fit the LDA to the training data
lda.fit(X_train, y_train)

# Predictions on testing data
lda_y_pred = lda.predict(X_test)

# Get accuracy score
accuracy_lda = accuracy_score(y_test, lda_y_pred)
print("Accuracy: ", accuracy_lda)
```

### QDA (Quadratic Discriminate Analysis)
```python
# 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 a QDA instance
qda = QuadraticDiscriminantAnalysis()

# Fit the QDA to the training data
qda.fit(X_train, y_train)

# Predictions on testing data
qda_y_pred = qda.predict(X_test)

# Get accuracy score
accuracy_qda = accuracy_score(y_test, qda_y_pred)
print("Accuracy: ", accuracy_qda)
```

### LogisticRegression
```python
from sklearn.linear_model import LogisticRegression

# 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 a logistic regression instance
log_reg = LogisticRegression()

# Fit the logistic regression to the training data
log_reg.fit(X_train, y_train)

# Predictions on testing data
log_reg_y_pred = log_reg.predict(X_test)

# Get accuracy score
accuracy_log_reg = accuracy_score(y_test, log_reg_y_pred)
print("Accuracy: ", accuracy_log_reg)
```
All three methods produce predictions and their accuracy scores.

My questions are:

1. How do LDA and QDA use the given data (`credit_df`) to compute the predictions?[/INST]