How to use Google Colab for Machine Learning Projects Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report The Google Colab is a cloud-based Jypyter notebook platform that can be used in Data Science. The colab platform is freely accessible to everyone and it auto-saves the projects. This allows us to run and train complex machine-learning models efficiently. It provides a user-interactive development environment, GPU(Graphical Processing Unit) and TPU(Tensor Processing Unit) access, storage on Google Drive, real-time collaboration, and numerous other advanced features. The GPU is designed for parallel processing which means it can support multitasking. Thus, this allows the distribution of training processes and helps maintain the speed of machine learning operations.In this article, we will discuss How to use Google Colab for Machine Learning Projects and also How to start Start Machine Learning Project in Colab. Google Colab Guide for Machine Learning ProjectsYou can open the colab by clicking the following step is as follows -Step 1: Create a Google account for the drive.Step 2: Open Google Drive and click " +new " in the top left corner.Step 3: Start navigating and click to more options and you will get the specified option(colab).Google drive linkStep 4: Click on Google Colaboratory to get the interface.Google ColaboratoryStep 5: In this Step, We will first discuss the upload of datasets in colab.Go to Files -> See the upload icon -> upload the file from your desktop files.Dataset in CollabStart Machine Learning Project in ColabLet’s implement the project of Gold Price Prediction using Machine Learning in a colab platform- Following are the set of Google Colab screenshots on implementing the machine learning project and its steps as follows -Step 1: Import the requirement library to run the overall performance of the project.Importing Libraries and DatasetPython libraries make it easy for us to handle the data and perform typical and complex tasks with a single line of code.Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.Numpy – Numpy arrays are very fast and can perform large computations in a very short time.Matplotlib/Seaborn – This library is used to draw visualizations.Step 2: Load the datasets using read_csv().Loading the DatasetStep 3: Use head() function to check the data of first five rows.Head FunctionCleaning and Preprocessing of dataStep 4: Now clean and preprocesses the data which identifying and correcting error in datasets such as total number of rows and column(df.shape), summary of dataframe(df.info), remove column index(df.drop), missing data values(df.isnull().sum()), duplicate values(df.duplicated().sum()) and co-relation of the column(df.corr()).Cleaning and Processing the data Step 5: Plot the graph using heatmap() function and set the size of the graph. Using heatmap it represent the relationships among two variables which are one plotted on particular axis.Plot() MapSplitting into Train and test dataStep 6: Perform training and testing of data which is used to execute the final model performance.Training and Testing the Data Model BuildingSklearn – This module contains multiple libraries are having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.Step 7: Start the model building by importing the module from "sklearn.preprocessing import StandardScaler" and use the function standardScaler() which determines the scaling of data.Module Building Step 8: Now import the specified module with sklearn library which helps to predict the gold price based on given datasets.Sklearn LibraryStep 9: Find the accuracy of data in which we have accuracy of 0.98 that consider good for practical applications.Step 10: Finally, predict the gold price and generate the result.Check AccuracyConclusionThe Google Colab platform is similar to Jupyter Notebook, but sometimes we have to go through the library installation in a code section, upload the file, and keep it on the drive. We get the information about the datasets through the code. As you can see, the working functionality of both the platform are same but the Jupyter Notebook has very advanced features. Comment More infoAdvertise with us Next Article Introduction to Machine Learning T tapasghotana Follow Improve Article Tags : Machine Learning Machine Learning Blogs Practice Tags : Machine Learning Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.Do you 5 min read Introduction to Machine LearningIntroduction to Machine LearningMachine learning (ML) allows computers to learn and make decisions without being explicitly programmed. It involves feeding data into algorithms to identify patterns and make predictions on new data. 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