example_classifier.py

import pandas as pd;
import numpy as np;
from sklearn.model_selection import train_test_split;
from sklearn.linear_model import LogisticRegression;
from sklearn.metrics import classification_report, confusion_matrix;

# Load the dataset
data = pd.read_csv('data.csv');

# separate features and target variable
X = data.drop('approved', axis=1);
y = data['approved'];

# Split the dataset 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);

# Deal with missing values (if any)
missing_value_replacement=X_train.mean();
X_train.fillna(missing_value_replacement, inplace=True);
X_test.fillna(missing_value_replacement, inplace=True);

# Train the logistic regression model
model = LogisticRegression();
model.fit(X_train,y_train);

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

# Evaluate the model
print("Confusion Matrix:");
print(confusion_matrix(y_test, y_pred));
print("\nClassification Report:");
print(classification_report(y_test, y_pred));

# Evaluate the model using accuracy score
from sklearn.metrics import accuracy_score;
accuracy = accuracy_score(y_test, y_pred);
print(f"\nAccuracy: {accuracy:.2f}");


print(f"{X_test.shape[0]} samples were tested, and the model achieved an accuracy of {accuracy:.2f}.")