Select Page

Advanced Machine Learning Projects for data science

by | Apr 24, 2024 | Data Science, Machine Learning

Machine learning and artificial intelligence are the most in-demand skills in today’s job market. Companies are expanding their data science teams to leverage the power of data, solve new problems, and improve business efficiency.

There are many high-growth roles like data scientist, machine learning engineer, and deep learning engineer across finance, healthcare, and marketing, among other sectors. However, lucrative opportunities often come with intense competition.

To break into these roles, first-hand experience on real-world datasets is essential. Tackling data science problems helps you develop your analytical, programming, and problem-solving skills. It also confronts you with challenges you’ll face while working in the industry, helping you excel at the data science interview.

That’s why, in this blog, I’ll share some advanced machine-learning project ideas to prepare you for practical applications and make your resume stand out!


Advanced Machine Learning Projects

In this blog, I have gathered a comprehensive list of advanced machine learning projects. Say goodbye to toy data sets like the Titanic or the House Sales Price. These project ideas will push your skills to new heights by confronting you with scenarios and use cases that resemble those you’ll be tackling in the industry.

Here is a list of the advanced machine learning projects that I’ll discuss through the rest of the blog:

  1. Forecasting Web-Traffic using Time-series Data (forecasting)
  2. Classify Brain tumor MRI Images using Deep learning (computer vision, classification)
  3. Optimize Train Schedules using Reinforcement Learning (Unsupervised learning and reinforcement learning)
  4. Implement Personalized Marketing by Predicting Customer Loyalty (Regression)
  5. Classify Insurance Claims to speed-up processing time (classification)
  6. Reduce the testing time of Cars by Feature Extraction (Dimensionality reduction)
  7. Image Segmentation for Disaster Resilience (computer vision, Image segmentation)
  8. Streamlining HR Processes through Employee Feedback Analysis (NLP, Sentiment Analysis)
  9. Building a product recommendation engine (Recommendation systems, Collaborative filtering)


Forecasting Web-Traffic Using Time-series Data

Forecasting is the process of predicting the future values of a time series. Time series are data points collected chronologically at regular intervals over time.

Time series forecasting has unique challenges with respect to tabular datasets in that we also need to consider the temporal aspect, which means that the observations are not independent.


Aim of the project

The aim of this project is to build a machine learning model to forecast the expected traffic for different web pages.



The Web Traffic Time Series Forecasting dataset was released by Google as part of a research competition on Kaggle. The dataset contains approximately 145k time series. Each of these time series contains the daily views of a different Wikipedia article, along with the source of the traffic (desktop/mobile).


Machine Learning skills

This dataset provides an excellent opportunity to revise your forecasting skills. You can try out widely used forecasting algorithms like Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) or Exponential Smoothing.

If you are into deep learning, you can test the effectiveness of long-short-term memory (LSTM) networks to forecast traffic.

Alternatively, you can tackle forecasting as a regression, extract a rich table of features from the past values of the times series, and then use traditional machine learning models like linear regression or gradient boosting machines to do the forecasts.

To learn how to forecast with traditional machine learning models, check out our Forecasting Specialization.

The forecasting model to use varies based on the characteristics of the time series. ARIMA and SARIMA are best for data with linear trends and seasonality. LSTM are suitable when dealing with nonlinear and non-stationary data and huge datasets. Traditional machine learning models are suitable for forecasting multiple time series with linear or non-linear patterns.

Regardless of the characteristics of the forecasting models themselves, take this project as an opportunity to practice those skills you know and learn some new ones if time permits.


Tools and frameworks

To implement these algorithms, you can use python packages like statsmodels, Prophet, and Keras/Tensorflow for neural network-based methods.

The good thing about Kaggle is that other people make their solutions public, so you can learn from what they’ve already tried. Check this notebook for an example of how to use ARIMA on this dataset. At this link, you can find more notebooks showing how various machine learning practitioners have approached the task.


Similar use cases you’ll find in the real world

The skills you learn from this ml project can be handy in many real-life scenarios, increasing your strengths as a data scientist. Forecasting is used by e-commerce giants like Amazon and flipkart to predict future traffic on different product pages.

In the financial domain, these skills are essential to modeling stock price movements and making investments.

Forecasting helps in predicting future demand, making it crucial in supply chain management and resource planning in healthcare and transportation.


Classify Brain tumor MRI images using Deep learning

Computer vision is the field of artificial intelligence that allows us to process and analyze information from digital images, videos, and other visual inputs. Image recognition techniques are used in healthcare to analyze medical images such as X-rays, MRIs, CT scans, and ultrasound images, providing critical insights that may escape the human eye.

Image classification is a computer vision technique where we train models to classify images into labels by extracting patterns and features from the images.

This project will help you understand how to perform image classification and how to tackle the common challenges that you’ll encounter in these type of projects. If you are comfortable with traditional machine learning, you can level up your skills with these deep learning projects.


Aim of the project

The aim of the project is to build a model using deep learning that can diagnose brain tumors from MRI (Magnetic Resonance Imaging) scans. Our aim is to detect if there is a tumor and then classify it in terms of grade, type, and specific location.



The Brain Tumor MRI Dataset is available to download for free on Kaggle. This dataset contains roughly 7k MRI images, which are classified into 4 classes: glioma, meningioma, no tumor, and pituitary.


Machine Learning skills

On this dataset, you can implement image classification by building convolutional neural network (CNN) models. CNNs are designed specifically for handling image datasets. The convolution layer can extract features like edges, exposure, and pixel matrix from the raw image.

Another approach you can try out is transfer learning. It involves using pre-trained models (on large datasets like ImageNet) and fine-tuning them to specific, often smaller, image datasets. This approach often achieves high performance with less data and computational resources.

As part of image classification, you can also implement object detection to locate the affected area in the MRI scans. You can explore advanced object detection frameworks like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). YOLO is incredibly fast because it processes the image in a single pass.

This project is an excellent way to learn image preprocessing techniques like normalization, data augmentation, and feature extraction.

Class imbalance is a big challenge, especially in medical datasets where the fraction of the ‘disease-positive’ class is very low. You can check out our Machine Learning with Imbalanced Data course if you’d like to learn different approaches to handling imbalanced classes.

I’d also suggest you spend time understanding the different components of deep learning architecture, like convolution, pooling layers, dropout, and fully connected layers. These components will allow you to control overfitting, increase accuracy, and reduce computational expenses.


Tools and frameworks

For data preprocessing in computer vision, you can use the OpenCV package. To implement CNNs or other deep learning-based models, TensorFlow, Keras, and PyTorch are some Python packages that provide functions and utilities.

Check out this Kaggle Notebook that shows how to implement a CNN using Pytorch on this dataset to get a head start.


Similar use cases you’ll find in the real world

Computer vision has vast applications in our everyday lives, and these skills are in high demand in healthcare, crowd surveillance, and security. The techniques you learn in this project can be expanded to include analyzing chest CT scans for tuberculosis and finding anomalies in X-rays.

Apart from healthcare, image classification has commercial uses, like classifying product images to enable visual search in e-commerce. Recently, image classification has been used in agriculture to identify pest-infected crops and monitor health.


Optimize Train Schedules using Reinforcement Learning

Reinforcement learning (RL) is an unsupervised learning technique where we do not have any labels to train the machine learning models on. The idea of reinforcement learning is inspired by behavioral psychology. In RL, we have an agent and specify a reward. The agent is made to try out different methods until it can maximize the reward. RL can be used to optimize transportation schedules; as we will see during this project.


Aim of the project

The aim is to use reinforcement learning-based AI agents that can manage train traffic in a simulated environment. We want to minimize the travel time for all trains to reach their destination.



The dataset was released as part of the Flatland Challenge in AI crowd platform. The challenge provides a simulated grid world environment for you to work on.


Machine Learning skills

The most commonly used techniques in reinforcement learning are Q-Learning, Deep Q-Networks (DQN), and actor-critic methods. At a more advanced level, we have multi-agent reinforcement learning, where multiple agents interact with each other and the environment. Examples include Q-Learning with Opponent Modeling (QLOM) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG).

Through this project, you will develop a fundamental understanding of mathematical concepts like Markov Decision Processes (MDPs), linear algebra, and Monte Carlo methods.

Learn how to handle sample efficiency to ensure effective policies with minimal interaction with the environment. You need to find the right trade-off, as overly conservative or aggressive exploration strategies can hinder learning. Addressing safety and ethical concerns is crucial when implementing them in real life.


Tools and Frameworks

OpenAI Gym provides a variety of environments for testing and benchmarking RL algorithms. TensorFlow, Pytorch, and RLlib (Reinforcement Learning Library) are Python packages that provide an implementation of RL algorithms. You can also use the TensorFlow Probability package’s inference tools for evaluation.


Similar use cases you’ll find in the real world

If you master using RL agents for train scheduling and rescheduling projects, you can apply these skills to a wide range of use-cases. Similar techniques are used in supply chain logistics optimization of shipping routes to minimize costs and reduce delays.

RL Agents are used in healthcare to optimize resource utilization by allocating hospital beds and scheduling appointments. On the commercial front, it is used in marketing for the best placement and targeting of online advertisements. Other applications include warehouse automation and energy management in power plants.


Implement personalized Marketing by predicting Customer Loyalty

Regression is the most widely used supervised machine learning technique. The fundamental idea of regression is to fit a model to the data points that best represent the relationship between the variables under consideration. In this project, we aim to use regression to predict if a particular offer will provide a benefit for a customer.

When you make payments at restaurants or apps like Amazon or Swiggy, you’ll notice that some cards offer you discounts. Most credit cards often provide personalized discounts to their customers to reduce churn rates and increase brand loyalty. Machine learning techniques are used to provide these personalized recommendations.


Aim of the project

The aim is to enhance the customer experience by leveraging machine learning algorithms to provide personalized promotions based on individual preferences and behaviors. The project aims to improve customer satisfaction, increase retention, and reduce unwanted campaigns.



The dataset was released as part of the Elo Merchant Category Recommendation on Kaggle. Elo, a major payment brand in Brazil, has built partnerships with merchants in order to offer promotions to cardholders.

The dataset provides historical transactions of all card members over 3 months, which can be used to understand their behavior patterns. It also provides transactions occurring at new merchants (restaurants or shops that the card member has not visited).


Machine Learning skills

This project is an amazing opportunity to polish your regression and statistics skillset. There are a wide variety of regression algorithms that can be used. You can start with a baseline linear regression model and then try out tree-based models like XGBoost, LightGBM, and Catboost.

Through this project, you can learn how to engineer meaningful features from the raw data to capture customer behavior and preferences. You should also understand the strengths and limitations of different regression algorithms and choose the right one. Learning techniques for model interpretation, like feature importance and partial dependence plots, will help you understand which factors are driving personalized promotions.


Tools and Frameworks

Pandas and Numpy libraries are used for data analysis, pre-processing, and manipulation. Scikit-learn is a powerful library that offers various regression algorithms, data preprocessing techniques, and model evaluation metrics. For data visualization, Matplotlib and Seaborn can be used to create informative plots and visualizations to better understand the data distribution.


Similar use cases you’ll find in the real world

The skills learned from the project of tailoring promotions using regression can be applied to various use cases across industries like retail, e-commerce, banking, and house price prediction, among others.

For instance, marketplaces like Amazon and Flipkart use similar methods to recommend products based on user’s search histories, purchase patterns, and product interactions. Food delivery apps like Swiggy offer discounts on specific cuisines or restaurants based on a user’s ordering history and preferences.


Classify Insurance Claims to speed-up processing cycle

Classification is a type of supervised learning in which a model is trained on a labeled dataset to categorize new observations into one of several classes.

In today’s fast-paced digital economy, insurance companies like BNP Paribas Cardif face the challenge of adapting their claims management processes to meet evolving needs. There’s a growing demand for faster and more efficient claims processing, which can be accomplished using machine learning techniques like classification.


Aim of the project

The aim is to leverage data science techniques to classify insurance claims early in the process for BNP Paribas Cardif. By accurately identifying claims that can be accelerated for faster payments and those requiring additional information, the company aims to streamline its claims management process and provide better service to its customers.



BNP Paribas Cardif has provided an anonymized database containing two categories of insurance claims: those that can be accelerated for faster payments and those requiring additional information before approval.


Machine Learning skills

This project will help you grasp fundamental concepts of binary classification, such as feature selection, model evaluation metrics (e.g., accuracy, precision, recall, F1-score), handling class imbalance, and hyperparameter tuning.

Challenges to tackle include dealing with imbalanced data in the insurance claims dataset, selecting the most suitable algorithms and features for classification, and optimizing model performance while considering the balance between interpretability and accuracy.

For this project, you can employ various machine learning algorithms for binary classification, such as logistic regression, decision trees, random forests, gradient boosting methods like XGBoost or LightGBM, and support vector machines (SVM). Additionally, ensemble methods and neural networks could also be explored to improve model performance.

To boost your machine learning skills, check out our Advanced machine learning specialization.

Train in data's advanced machine learning specialization.

Tools and Frameworks

To implement logistic regression or tree-based models, Scikit-learn is a popular choice of library. It provides an easy interface to train and test these models, along with built-in functionalities like feature importance. To try out neural network-based approaches, you can use Tensorflow or PyTorch.


Similar use cases you’ll find in the real world

The skills acquired from this project can be applied across various real-life scenarios, such as fraud detection in finance, customer churn prediction, and categorizing movies on Netflix. Classification is extensively used in manufacturing to anticipate equipment failures or maintenance needs based on early indicators.


Reduce the testing time of Cars by Feature Extraction

Feature extraction is a process used in machine learning to transform raw data into a set of numerical features that can be used to train a machine learning model.

Some datasets are huge in terms of size and number of features. Reducing computation expenses is a critical challenge, and the size of the datasets may play a critical role.

Mercedes-Benz released the challenge of optimizing testing time for its vast array of car configurations.


Aim of the project

Automobile manufacturers have to perform safety testing on all their models. The diverse available options in steering, wheels, and dashboards leads to a vast number of car configurations, and each of them needs to be tested.

The aim of this project is to leverage machine learning for feature extraction and dimensionality reduction and enhance the efficiency of testing. This project aims to predict the testing time for different car feature permutations, contributing to quicker testing and reduced carbon dioxide emissions.

Side note: Car manufacturers also doctor their results. Check VW diesel scandal for example.



The dataset consists of features representing custom specifications of Mercedes Benz cars, with each feature anonymized. These predictors include both categorical variables denoted by letters and binary variables denoted by 0/1 values. Examples of features could be 4WD, added air suspension, or a head-up display. The label/target is the time taken for each car to pass testing based on its specific feature configuration.


Machine Learning skills

This project is perfect for you to learn about different feature extraction techniques and when to use them. Various algorithms, like principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE), can be used for dimensionality reduction.

Additionally, you can employ regression algorithms like linear regression, decision trees, and ensemble methods for prediction tasks.

You may face challenges in addressing the curse of dimensionality, handling large datasets efficiently, and optimizing model performance. You need to find the trade off between dimensionality reduction and not losing model interpretability, to ensure the model is explainable.


Tools and Frameworks

Open-source Python packages such as Scikit-learn for machine learning algorithms, Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization are essential for this project.

You can find source code for analysis and models created by other practitioners at this link.


Similar use cases you’ll find in the real world

Dimensionality reduction techniques can help in analyzing complex medical datasets for disease diagnosis and treatment planning. They are also applied in optimizing production processes and quality control in manufacturing industries, analyzing sensor data for pollution control and resource management.


Image segmentation for disaster resilience

This project will help you explore high-level tasks in computer vision. Image segmentation is a computer vision technique that involves dividing a digital image into multiple segments (sets of pixels) to create a meaningful representation. For example, in an image of a street scene, segmentation will label pixels as ‘road’, ‘car’, ‘pedestrian’, or ‘building’, among other things.

In this project, you’ll explore performing image segmentation to create a blueprint of the city infrastructure.


Aim of the project

The drone imagery from satellites can be used to create building footprints of a city or region. Building footprints refer to the outlines of the ground area covered by buildings as seen from above. These footprints represent the exact shape and boundary of a building on the earth’s surface. This information helps in planning effective responses to disasters, such as floods or earthquakes, by understanding the layout and density of buildings in potentially affected areas.



The dataset comprises drone imagery from 10 cities and regions across Africa, paired with building footprints annotated with the assistance of local OpenStreetMap communities. The imagery is provided as Cloud Optimized GeoTiffs (COG) with varying spatial resolutions, including red, green, blue, and alpha bands.

The task here is segmentation to classify the presence or absence of a building on a pixel-by-pixel basis in the aerial imagery.


Machine Learning skills

Machine learning algorithms commonly used for segmentation tasks include convolutional neural networks (CNNs) such as U-Net, DeepLab, and SegNet.

This project can be a challenge for beginners, as you would need to handle spatial data formats. Also, there is a lot of variability in building sizes, shapes, and scales, which makes it difficult for the model to accurately identify boundaries. Learn strategies to mitigate class imbalances, such as data augmentation or weighted loss functions. You will also learn techniques specific to computer vision, like handling low-resolution images.


Tools and Frameworks

Python packages like TensorFlow or PyTorch can be used for building and training deep learning models. There are also specific packages like GeoPandas for handling GeoJSONs and spatial data, Rasterio for working with geospatial imagery, and scikit-image for image processing tasks.


Similar use cases you’ll find in the real world

Skills acquired from this project can be applied in real-life scenarios such as disaster response planning, urban development monitoring, infrastructure management, and environmental conservation efforts. For example, accurate building segmentation can facilitate rapid damage assessment after natural disasters, enabling timely rescue and relief operations.


Streamlining HR processes through NLP-driven Employee Feedback Analysis

Natural Language Processing (NLP) is a field of artificial intelligence that enables computers to understand, interpret, and generate human language in a meaningful way.

In today’s digital world, there’s a lot of information available through various textual data sources like social media posts, tweets, and reviews. In any organization, employee feedback reviews can be used to identify employee sentiments, preferences, and concerns using natural language processing (NLP).


Aim of the project

We want to revolutionize HR processes by leveraging natural language processing (NLP) techniques to analyze employee feedback data. This can empower HR departments to make data-driven decisions, optimize employee experiences, and create a productive work environment.

Some of the tasks you’ll work on in this project are topic modeling, sentiment analysis and text summarization.



The Employee Job Satisfaction Insights dataset is a collection of employee reviews across various job roles and locations, obtained through web scraping from AmbitionBox. It includes attributes such as job title, text reviews, geographical location, employment status, department, and ratings on aspects like work-life balance, skill development, salary, and job security.


Machine Learning skills

Topic modelling can help uncover latent topics within the text data, enabling HR professionals to identify prevalent themes in feedback, such as work-life balance and career growth. Algorithms such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) can be employed for this purpose.

Sentiment Analysis can be performed using algorithms like Naive Bayes, Support Vector Machines (SVM), or Recurrent Neural Networks (RNNs). By identifying positive, negative, or neutral sentiments, it is easier to identify areas of concern.

Text summarization can be applied to generate concise summaries of employee reviews, facilitating actionable insights. You can use LSTM-based approaches or generative LLMs like GPT and Mistral to implement it.


Tools and Frameworks

Python libraries and tools that can be used for implementing these algorithms include:

  • For Topic Modeling: Gensim, sklearn (for LDA, NMF), Spacy
  • For Sentiment Analysis: NLTK, TextBlob, VADER Sentiment, scikit-learn
  • For Text Summarization: NLTK, Gensim, Sumy, Huggingface


Similar use cases you’ll find in the real world

The skills learned in the project by manipulating text data are useful across industries and organizations. The retail, hospitality, and e-commerce industries use sentiment analysis techniques to analyze customer feedback from reviews, surveys, and social media. Investment firms utilize sentiment analysis to analyze news articles, gauge market sentiment, and predict stock market movements. Text data can also be used to train chatbots for customer service and scheduling appointments. NLP can also be used to detect fake news.


Building a product recommendation engine

Recommender systems, also known as recommendation engines, are a class of machine learning systems that predict the likelihood that a user would prefer an item or make a particular choice among a collection of options. These systems are designed to help companies offer personalized suggestions to users, enhance the user experience, and optimize engagement across various platforms. This project will let you explore different approaches to building a recommendation engine for a financial company.


Aim of the project

Santander Bank is dedicated to enhancing its customer service by providing targeted financial product recommendations. As the financial journey of every individual varies, personalized financial advice and product offerings are crucial. For example, a person taking a home loan for the first time and another customer taking a loan on existing collateral would prefer different products.

The aim is to build a machine-based recommendation engine to deliver personalized product recommendations more effectively across its entire customer base.



The dataset is available on Kaggle. We are given 1.5 years of anonymized customer behavior data from Santander Bank, spanning from January 2015 to June 2016. The challenge is to predict the new products customers will purchase in June 2016, based on their existing product portfolio as of May 2016.


Machine Learning skills

Recommendation engines can be built using various methods, with the most common being collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering predicts items based on user interaction patterns. It identifies other users whose tastes match those of a specific user, and recommends products they have liked in the past. Check out this sample notebook which shows the implementation of collaborative filtering on this dataset.

Content-based filtering recommends items using features of the items themselves, tailored to a user’s previous likes or profile attributes. Hybrid systems combine both methods to enhance recommendation accuracy and overcome the limitations inherent in individual approaches.


Tools and Frameworks

For building and implementing recommendation systems, several Python libraries can be incredibly useful.

  1. NumPy and Pandas for data manipulation and analysis.
  2. Scikit-learn offers support for building basic recommendation systems using clustering, regression, and classification algorithms.
  3. Surprise is a package specifically built for building and analyzing recommender systems that support collaborative filtering. It provides tools to directly build and evaluate different recommendation algorithms.
  4. TensorFlow Recommenders (TFRS): An extension of TensorFlow that is specifically designed for building complex recommendation models
  5. LightFM provides Python implementations of a number of popular recommendation algorithms for both collaborative filtering and content-based recommendations.


Similar use cases you’ll find in the real world

Recommendation systems are pivotal in industries like e-commerce and retail. Companies like Netflix and Spotify utilize both collaborative filtering and content-based filtering to personalize media content offerings. Websites like Google News use hybrid recommendation systems to personalize news feeds and articles. Facebook and LinkedIn use recommendation systems to suggest friends or professional contacts based on mutual connections, common interests, and similar activities on the platform.



If you reached this far, thank you for reading. Hopefully, you’ll find some of these advanced machine learning projects for beginners looking to expand their skillset useful.

To boost your machine learning skills, check out our Advanced machine learning specialization. In our courses, you’ll find plenty of theory and practical code hosted in multiple Github repositories that will allow you to learn more about feature engineering, feature selection, hyperparameter tuning and interpreting machine learning models, among other things.

Train in data's advanced machine learning specialization.

Getting your hands on noisy and big datasets is the only road to success. So get started today!