Building a Simple Machine Learning Model in Python with Scikit-Learn

Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In this article, we will explore how to build a simple machine learning model in Python using the popular library, Scikit-Learn.

Step 1: Install Scikit-Learn

To get started, you will need to install Scikit-Learn on your computer. You can do this using the following command in your terminal:

pip install scikit-learn

Step 2: Import the Necessary Libraries

Next, you will need to import the necessary libraries in your Python script. This includes importing Scikit-Learn and any other libraries that you will be using for data manipulation and visualization.


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


Step 3: Load and Prepare the Data

For this example, let’s say we have a dataset containing information about customers and whether they purchased a product or not. We can load the data into a pandas DataFrame and then split it into features (X) and target variable (y).


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

# Split the data into features and target variable
X = data.drop('Purchased', axis=1)
y = data['Purchased']

# 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)


Step 4: Build and Train the Model

Now, we can build our machine learning model using Scikit-Learn. For this example, let’s use a simple logistic regression model.


# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Initialize the model
model = LogisticRegression()

# Train the model
model.fit(X_train, y_train)


Step 5: Evaluate the Model

Finally, we can evaluate our model’s performance by making predictions on the testing set and calculating the accuracy.


# Make predictions
y_pred = model.predict(X_test)

# Calculate the accuracy
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)


And that’s it! You have now built a simple machine learning model in Python using Scikit-Learn. This is just the beginning, and there are many more algorithms and techniques to explore in the world of machine learning.