Master Data Science & Machine Learning: The Best Kaggle Projects for Beginners in 2026
If you are looking to break into the IT sector as a data professional, you will likely find that reading through textbooks as well as watching tutorials on how to use software is not enough to guarantee you a job. What you need to do in order to prove your worth to potential employers is to put your skills into practice with real data. One of the best places to do this is on the world’s largest data science community site, Kaggle. Below we have outlined the best Kaggle projects for beginners in order to help build your skills, create a portfolio that will stand out from the rest, and earn you a high-paying job in the IT sector.
Top 3 Simple Projects for Beginners with Machine Learning.
Start with the simple stuff. Instead of diving into the complex world of deep learning or even complex convolutional networks for images, start by working with simple tabular data. This type of data will go through a well-defined workflow of loading the data, handling any missing information, training a classifier or regressor to predict information, and then testing the model to see how well it did.
- Titanic—Machine Learning from Disaster: With this classic binary classification challenge, you can train a machine learning model to predict which passengers survived the Titanic disaster. The characteristics of the passengers, like the age of the passengers, their gender, and the class of their tickets, are all used for training and testing the model. This challenge is ideal for learning how to do feature engineering for a binary classification problem.
- House Prices—Advanced Regression Techniques: This regression challenge is based on the Ames Housing dataset. You will have to deal with missing values and encode categorical data. Furthermore, you will apply advanced regression techniques like random forests to predict the sales prices of houses.
- Iris Species Dataset: This is a multi-class classification problem where you can develop a system to recognize different Iris species based on their physical characteristics. This is the perfect “hello world” type of problem for machine learning, using simple algorithms like K-Nearest Neighbors (KNN) to create a model.
What are the best data science datasets for beginners on Kaggle?
In order to be able to create predictions with data, first one has to learn how to work with this data (cleaning, changing data forms, and analyzing data). The best way to learn this is by working with very clean, highly graphical, and very interesting data. The data should not require too many advanced statistical transformations in the initial stages.
- Netflix Movies and TV Shows Dataset: A really good dataset for practicing exploratory data analysis (EDA). You can find out the trends of Netflix content production, create a map of movie lengths, and also find out which country is producing the most content using only Pandas and Seaborn libraries, for example.
- World Happiness Report: A simple-to-use, mostly tabular dataset of countries ranked by how happy the citizens are. Great for learning to create data visualizations, understanding correlation analysis, and simple linear regression models using libraries such as Pandas and Seaborn for data manipulation and visualization.
- Students' Performance in Exams: This is a very clean dataset for practicing how demographic data of students and their scores in different exams affect each other. One possible application is to find out how the education level of the parents and the amount of time a student studies for his/her exams affect his/her academic success.
How SevenMentor Can Help You Turn These Data Science / Machine Learning / AI Projects into High Paying Career / Jobs.
While practicing with the public and navigating the fierce battle to get IT jobs in the market can be done independently by anyone, that alone is not enough. The need for a mentor, industry-recognized certification, and, most importantly, hands-on experience cannot be fulfilled by independently practicing on Kaggle by anyone. And this is where SevenMentor Institute comes into the picture—the best to bridge the gap between a beginner and a job-ready data scientist to get a high-paying job in the market.
As the salary of the data experts skyrockets to an all-time high in 2026, it shall be very challenging for the self-taught developers to clear the technical round of the HR process. SevenMentor Institute helps candidates to not only run with the code templates but also to understand the mathematics behind the model, real-life business applications, and how to deploy the model. Our team of industrial experts helps you to create the production-ready Kaggle solution, which can be put on your resume to get you selected by top companies across the globe.
What are the 7 Best Kaggle Machine Learning Projects for 2026?
There are many public datasets that have been used by most data scientists. To make your portfolio stand out from the crowd in 2026, you should stick to a selection of 7 handpicked Kaggle datasets for this tutorial on modern data science engineering with 7 large practical projects that you can download with one click to practice with them.
- Customer Churn Prediction (Telco Dataset): Group customers into segments and predict if they are likely to cancel their subscription. This project is very attractive to employers as it solves a real, high-value problem for businesses.
- Store Sales - Time Series Forecasting: By utilizing past sales from various supermarkets, an engineer would be able to forecast the future demands of various products with the use of algorithms such as XGBoost, Prophet, and ARIMA.
- Mall Customer Segmentation: Unsupervised clustering (e.g., K-Means) of mall customers based off of their spending scores and annual income.
- SMS Spam Collection (Sentiment & Text Classification): Build a Natural Language Processing (NLP) pipeline using TF-IDF or BERT to classify messages as spam or ham in this real-world problem.
- Credit Card Fraud Detection: The data set provided for this project contains credit card transactions that include several variables that were used to train a model to detect fraudulent transactions. By working on this project, you will learn to deal with highly imbalanced data and understand key metrics, such as precision-recall and ROC-AUC curves, for evaluating the performance of a classifier.
- Digit Recognizer (MNIST Dataset): This is a great place to start with deep learning and computer vision by building a convolutional neural network (CNN) to classify handwritten digits.
- Forest Cover Type Prediction: This project is a multi-class classification problem that predicts the forest cover types based off of the geographical features of a region. It is a challenging problem that requires the use of a large number of hyperparameters and careful tuning in order to achieve the optimal results.
SevenMentor vs. Generic Online Bootcamps: Why We Stand Out
Choosing where to invest your time and education is a massive decision. Here is a direct comparison of what you get when training with SevenMentor versus generic, self-paced online courses:
How Do You Build a Winning Portfolio Using Kaggle?
Just completing a notebook in Kaggle is not enough for getting hired. Many people, who want to become developers, fail to present their findings in a sufficient way to non-technical people in the company.
- Clean code + written-out assumptions: In your markdown cells explain your work. Why did you choose a certain preprocessing step? Why did you choose a certain model?
- Highlight the business value to the company—show how your model can save money, increase customer retention, optimize inventory, etc.
- Highlight the business value to the company - Show how your model can save money, increase customer retention, optimize inventory, etc. Include links to deployed models where applicable.
Why Should Beginners Start with Kaggle?
In the next article I will explain why, in my humble opinion, starting to learn to develop models to work with big data, with complex tasks (computer vision, natural language processing), on a massive scale, should start with Kaggle.
┌─────────────────────────────────────────┐
│ The Kaggle Ecosystem │
└────────────────────┬────────────────────┘
│
┌─────────────────────────────┼─────────────────────────────┐
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Real Datasets │ │ Free Clouds │ │ Community Code │
│ Clean & messy │ │ No install, free│ │ Learn from top │
│ real scenarios │ │ GPU/TPU power │ │ notebooks │
└─────────────────┘ └─────────────────┘ └─────────────────┘
- No Setup Required: Jupyter Notebooks.With all the main data analysis libraries (Pandas, NumPy, Scikit-learn, etc.) already pre-installed on Kaggle for you, you can get started with your learning right away without the setup hassle of local Jupyter Notebooks on your computer.
- Active Learning Community: Competitions on Kaggle have a “Discussion” and “Code” section for users to ask for help. On Kaggle you can also find thousands of public notebooks (Kernels) that have been written by expert kagglers and can be used for learning and reference, including exactly how they went about solving a problem in a competition.
- Kaggle notebooks are organized by difficulty and by type of competition, so there are many ways to start learning to apply your skills. For example, there are “Getting Started” competitions designed for learners, and you don’t have to enter a competition to work on a notebook.
A Step-by-Step Strategy to Tackle Any Kaggle Project
It can be blank code cells in a freshly opened notebook that fills one with trepidation. Step by Step Way to work on Machine Learning Project.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ 1. Import & EDA │ ──> │2. Data Cleaning │ ──> │3. Feature Eng. │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ 6. Submit/Deploy│ <── │5. Model Eval. │ <── │4. Model Training│
└─────────────────┘ └─────────────────┘ └─────────────────┘
- - Exploratory Data Analysis (EDA): - First step of the strategy is to perform an Exploratory Data Analysis (EDA) on the data. The goal of EDA is to learn about the basic distribution of the variables in the data and the characteristics of the data has. It’s usually done by first loading the data into a pandas Data Structure and then using functions like .info() and .describe() to get an overview of the data. Additionally plotting the distribution of the single variables can help a lot to get a feeling for the data. Also it’s good to know how many missing values there are and if there are any outliers in the data. For creating the distribution plots of the single variables Seaborn-Plots can be used.
- Data Cleaning: In this section, you would clean missing information within your data. Decide whether to drop certain columns (with too many missing values, for instance) or fill in the missing information in a sensible manner.
- Feature Engineering: Turn raw input fields into useful variables that your model can learn from. For example, you might transform highly skewed distributions into a more reasonable form, turn very high-dimension continuous variables into binned / bucketed form, and generally convert all of your categorical variables into encoded form.
- Model Training: You should start with a simple baseline, such as a logistic regression or a linear regression, and then progress to more complex models such as a random forest or a gradient boosting model (XGBoost, LightGBM, etc.).
- Model Evaluation: For evaluating a model, use a validation technique such as $K$-Fold Cross-Validation, and not just the accuracy of the model. Use metrics to evaluate the model.Also take a look at the model’s precision, its recall, and the F1-score as well as the model’s ability to rank items with the ROC-AUC.
Got Questions? Here Are Some FAQs
1. Is Kaggle entirely free to use?
Kaggle is free for everyone. All the data sets, community notebooks, discussion forums, and even the cloud-based GPU/TPU compute resources can be used for free.
2. Can I get a job just by doing Kaggle competitions?
While winning the competition is prestigious and looked upon favorably by the employers, they look for candidates with end-to-end engineering skills. A beginner with a well-documented portfolio of his/her projects (beginner to intermediate) will get a job over someone who keeps on climbing the leaderboard.
3. Which programming language is best for Kaggle?
Python is the most popular language on Kaggle. This is because of the huge range of libraries available for data manipulation, modeling, and more. For data manipulation there is Pandas, for modeling there is Scikit-Learn, and for deep learning there is PyTorch. These libraries are used by the vast majority of data scientists. So for a beginner the best language to learn is Python.
4. Do I need advanced math to start with Kaggle?
A beginner does not need to have a PhD in math to start to create models in Kaggle competitions. Most of the models used in competitions need only high school level algebra, some linear algebra, and basic statistics (e.g. mean, median, mode, standard deviation, etc).
5. Why SevenMentor instead of learning alone?
Free resources to learn for all and although there are a lot of free sources available to learn from, the way of learning has many holes without a proper structure to follow to learn with a right approach, to get recognized for what you can do and to get placed in a right job in a right time without wasting time in search of jobs. SevenMentor bridges this gap by imparting training in data science with direct industry mentor, with a proper structured course, to review the projects done by student and to place them in right job with the help of active and dedicated placement cell that helps students to get placed in top MNCs of the world.
6. Does SevenMentor assist with job placements for Data Science graduates?
Yes! We at SevenMentor have an active, dedicated placement cell. We have a set of processes which enables to prepare you for interviews, hold Mock tests, Modify your Resume & Kaggle Notebook Portfolio so that you get placed in Top IT Companies globally.
blog Links:
Do visit our channel to know more: SevenMentor
SevenMentor
Expert trainer and consultant at SevenMentor with years of industry experience. Passionate about sharing knowledge and empowering the next generation of tech leaders.