We’ll build a machine learning model that predicts whether a student will pass an exam based on various factors such as study hours, attendance, test preparation course, etc.

You’ll learn how to:
● Load and understand real-world data
● Clean and preprocess the data
● Use NumPy and Pandas
● Build a machine learning model using Logistic Regression
● Evaluate the model using Accuracy, Precision, Recall, and F1 Score
● Make predictions

We'll use a small mock dataset for this project (you can replace it with any CSV file if needed):

import pandas as pd
# Sample dataset
data = {
'study_hours': [2, 3, 4, 5, 6, 1, 3, 7, 8, 9],
'attendance': [60, 70, 75, 80, 85, 50, 65, 90, 95, 98],
'preparation_course': ['no', 'yes', 'yes', 'no', 'yes', 'no',
'no', 'yes', 'yes', 'yes'],
'passed': [0, 0, 1, 0, 1, 0, 0, 1, 1, 1]
}
df = pd.DataFrame(data)
print(df)

print(df.info())
print(df.describe())
print(df['preparation_course'].value_counts())

Here, passed is the target variable (0 = fail, 1 = pass). We need to convert preparation_course from categorical to numerical.

Convert 'preparation_course' to numeric using one-hot encoding:

df['preparation_course'] = df['preparation_course'].map({'no': 0,
'yes': 1})

We separate features and labels, then split data into training and testing sets.

from sklearn.model_selection import train_test_split
X = df[['study_hours', 'attendance', 'preparation_course']]
y = df['passed']
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.3, random_state=42)

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print("Predictions:", y_pred)

from sklearn.metrics import accuracy_score, precision_score,
recall_score, f1_score, confusion_matrix
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))
print("F1 Score:", f1_score(y_test, y_pred))
print("Confusion Matrix:\\n", confusion_matrix(y_test, y_pred))

import matplotlib.pyplot as plt
import seaborn as sns
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()

new_student = [[4, 80, 1]]
result = model.predict(new_student)
print("Will the student pass?", "Yes" if result[0] == 1 else "No")

In this project, we learned how to:
1. Pandas for data manipulation
2. NumPy-style indexing, mapping
3. Preprocessing & Encoding
4. Logistic Regression (Classification)
5. Train-test split
6. Evaluation metrics: Accuracy, F1 etc.
7. Confusion Matrix + Seaborn Visual