How to Install Sklearn in Colab Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Google Colab is a cloud-based Jupyter notebook environment that allows you to write and execute Python code in the browser with zero configuration required. It offers free access to computing resources, including GPUs and TPUs, making it an excellent platform for machine learning and data science projects. Scikit-learn is a popular machine learning library in Python that provides simple and efficient tools for data mining and data analysis. This guide will walk you through the process of installing and using Scikit-learn in Google Colab.Table of ContentWhy Use Google Colab for Scikit-learn?Setting Up Google ColabStep 1: Access Google ColabStep 2: Create a New NotebookInstalling Scikit-learn in Google ColabStep 1: Install Scikit-learnStep 2: Verify the InstallationWhy Use Google Colab for Scikit-learn?Free Access to Powerful Hardware: Colab provides free access to GPUs and TPUs, which can significantly speed up your machine learning workflows.Pre-Installed Libraries: Many popular libraries, including Scikit-learn, are pre-installed, saving you from the hassle of installation and setup.Collaboration: Easily share notebooks with others and collaborate in real-time.Convenience: Run notebooks directly in your browser without any local setup.Setting Up Google ColabStep 1: Access Google ColabOpen your web browser and go to Google Colab.Sign in with your Google account if you are not already logged in.Step 2: Create a New NotebookOnce you are in Google Colab, click on File > New Notebook. This will create a new Jupyter notebook where you can start writing and executing your code.Installing Scikit-learn in Google ColabWhile Scikit-learn is usually pre-installed in Google Colab, you might want to manually install it to ensure you have the latest version or if it's not available. Follow these steps:Step 1: Install Scikit-learn!pip install -U scikit-learnor!pip install https://codeload.github.com/Santosh-Gupta/scikit-learn/zip/refs/heads/master Step 2: Verify the InstallationTo verify that Scikit-learn has been installed correctly, you can import it and check its version with the following code: Python import sklearn print("Scikit-learn version:", sklearn.__version__) Run the CellPress Shift + Enter to execute the cell. This will install Scikit-learn (if it wasn't already installed) and print its version to confirm the installation. Create Quiz Comment P poonamvbo5 Follow 0 Improve P poonamvbo5 Follow 0 Improve Article Tags : Machine Learning Data Science Blogathon 2024 Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning7 min readWhat is Machine Learning Pipeline?6 min readApplications of Machine Learning3 min readPython for Machine LearningMachine Learning with Python Tutorial5 min readNumPy Tutorial - Python Library3 min readPandas Tutorial4 min readData Preprocessing in Python4 min readEDA - Exploratory Data Analysis in Python6 min readFeature EngineeringWhat is Feature Engineering?5 min readIntroduction to Dimensionality Reduction4 min readFeature Selection Techniques in Machine Learning4 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning14 min readLogistic Regression in Machine Learning10 min readDecision Tree in Machine Learning8 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers6 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering â Introduction6 min readHierarchical Clustering in Machine Learning6 min readDBSCAN Clustering in ML - Density based clustering6 min readApriori Algorithm6 min readFrequent Pattern Growth Algorithm5 min readECLAT Algorithm - ML5 min readPrincipal Component Analysis (PCA)7 min readModel Evaluation and TuningEvaluation Metrics in Machine Learning9 min readRegularization in Machine Learning5 min readCross Validation in Machine Learning5 min readHyperparameter Tuning5 min readUnderfitting and Overfitting in ML3 min readBias and Variance in Machine Learning6 min readAdvanced TechniquesReinforcement Learning9 min readSemi-Supervised Learning in ML5 min readSelf-Supervised Learning (SSL)6 min readEnsemble Learning8 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code5 min read Like