Python machine learning

 

Python machine learning example with results

Sure. Here is a simple Python Machine Learning example with results that is useful for beginners.

Example: Predict Student Pass/Fail using Machine Learning

We will use Logistic Regression to predict whether a student will pass based on study hours.

1. Install libraries

pip install pandas scikit-learn

2. Python Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Dataset
data = {
    "StudyHours": [1, 2, 2.5, 3, 4, 5, 5.5, 6, 7, 8],
    "Result":    [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
}

df = pd.DataFrame(data)

print("Dataset:")
print(df)

# Input and output
X = df[["StudyHours"]]
y = df["Result"]

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Create model
model = LogisticRegression()

# Train model
model.fit(X_train, y_train)

# Test model
y_pred = model.predict(X_test)

# Accuracy
accuracy = accuracy_score(y_test, y_pred)

print("\nAccuracy:", accuracy)

# Predict new students
new_students = pd.DataFrame({
    "StudyHours": [2, 3.5, 5, 7]
})

predictions = model.predict(new_students)

print("\nPredictions:")

for hours, result in zip(new_students["StudyHours"], predictions):
    if result == 1:
        print(hours, "hours -> PASS")
    else:
        print(hours, "hours -> FAIL")

3. Sample Result

Dataset:
   StudyHours  Result
0         1.0       0
1         2.0       0
2         2.5       0
3         3.0       0
4         4.0       1
5         5.0       1
6         5.5       1
7         6.0       1
8         7.0       1
9         8.0       1

Accuracy: 1.0

Predictions:
2.0 hours -> FAIL
3.5 hours -> FAIL
5.0 hours -> PASS
7.0 hours -> PASS

How Machine Learning works here

Training Data → Algorithm → Trained Model → Prediction

Study Hours
     ↓
Machine Learning Model
(Logistic Regression)
     ↓
     Result
   PASS / FAIL

For example:

Study Hours	Prediction
2 hours	❌ FAIL
3.5 hours	❌ FAIL
5 hours	✅ PASS
7 hours	✅ PASS

Note: This is a teaching example with a very small artificial dataset, so the 100% accuracy should not be interpreted as real-world performance.

If you want, I can also give you a real-world Python ML project with dataset, code, output/results, graphs, and explanation such as House Price Prediction, Student Placement Prediction, Diabetes Prediction, or Sales Prediction.