Setting Up Scikit-learn and Training Your First Model

Scikit-learn is a Python library that provides algorithms for classification, regression, clustering, preprocessing, and model evaluation.

Installing Scikit-learn

Install Scikit-learn and its commonly used dependencies:

pip install scikit-learn pandas numpy

Import the libraries required for a basic machine learning workflow:

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_absolute_error, r2_score

Creating a Sample Dataset

Suppose you want to predict a student's examination score based on the number of hours spent studying.

df = pd.DataFrame({

"Study_Hours": [1, 2, 3, 4, 5, 6, 7, 8],

"Exam_Score": [35, 40, 50, 55, 65, 70, 80, 90]

})

X = df[["Study_Hours"]]

y = df["Exam_Score"]

print(df)

Here, X contains the input feature, while y contains the target that the model will learn to predict.

Splitting the Dataset

The model should be evaluated on observations that were not used for training.

X_train, X_test, y_train, y_test = train_test_split(

X,

y,

test_size=0.25,

random_state=42

)

print("Training records:", len(X_train))

print("Testing records:", len(X_test))

The test_size=0.25 setting reserves 25% of the observations for testing. The random_state parameter makes the split reproducible.

Training the Model

model = LinearRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Predictions:", predictions)

print("Actual values:", y_test.tolist())

The fit() method trains the model using the training data. The predict() method generates estimates for new input values.

Because this is a very small demonstration dataset, its results should not be treated as evidence of real-world predictive performance.