100+ Machine Learning Projects with Source Code [2025] Last Updated : 22 Aug, 2025 Comments Improve Suggest changes Like Article Like Report This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning and Data Science.Machine Learning Project for Beginners Once you’ve learned the basics of machine learning, it’s important to try out some practical projects to strengthen your skills. This section includes fun and simple machine learning projects for beginners that you can quickly pick up to build a strong foundation.1. Text and Image ProcessingMachine Learning can understand text and images. From detecting spam emails to recognizing handwritten digits or even coloring old black-and-white photos, these projects show how ML works with everyday data.Detecting Spam Emails Using Tensorflow in PythonSMS Spam Detection using TensorFlow in PythonPython | Classify Handwritten Digits with TensorflowOCR of Handwritten digits | OpenCVRecognizing HandWritten Digits in Scikit LearnIdentifying handwritten digits using Logistic Regression in PyTorchCartooning an Image using OpenCV – PythonCount number of Object using Python-OpenCVCount number of Faces using Python – OpenCVText Detection and Extraction using OpenCV and OCRCIFAR-10 Image Classification in TensorFlowBlack and white image colorization with OpenCV and Deep LearningHandwritten Digit Recognition using Neural Network2. Social Media and Sentiment AnalysisPeople express their opinions on social media every day. Machine Learning can study these posts to understand whether people feel positive, negative or neutral about a topic.Twitter Sentiment Analysis using PythonFacebook Sentiment Analysis using python3. Finance and EconomicsThe financial world deals with huge amounts of data every day. Machine Learning can be used to detect fraud, predict stock and cryptocurrency prices and even estimate housing values. These projects show how ML can help make smarter financial decisions.Credit Card Fraud DetectionDogecoin Price Prediction with Machine LearningZillow Home Value (Zestimate) Prediction in MLBitcoin Price Prediction using Machine Learning in PythonOnline Payment Fraud Detection using Machine Learning in PythonStock Price Prediction using Machine Learning in PythonStock Price Prediction Project using TensorFlowMicrosoft Stock Price Prediction with Machine LearningPredicting Stock Price Direction using Support Vector MachinesShare Price Forecasting Using Facebook Prophet4. Retail and CommerceShops and businesses want to know what customers like, how much they will spend and how to improve sales. Machine Learning can help by forecasting sales, analyzing product prices, grouping customers and even studying online reviews.Sales Forecast Prediction – PythonCustomer Segmentation using Unsupervised Machine Learning in PythonAnalyzing selling price of used cars using PythonBox Office Revenue Prediction Using Linear Regression in MLFlipkart Reviews Sentiment Analysis using PythonLoan Approval Prediction using Machine LearningLoan Eligibility prediction using Machine Learning Models in PythonHouse Price Prediction using Machine Learning in PythonML | Boston Housing Kaggle Challenge with Linear Regression5. HealthcareMachine Learning is helping doctors and researchers predict diseases earlier and more accurately. These projects focus on health problems like heart disease, cancer, Parkinson’s and autism, showing how data can be used to save lives.Disease Prediction Using Machine LearningML | Heart Disease Prediction Using Logistic RegressionPrediction of Wine type using Deep LearningParkinson’s Disease Prediction using Machine Learning in PythonML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic RegressionML | Cancer cell classification using Scikit-learnML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross-ValidationAutism Prediction using Machine LearningMedical Insurance Price Prediction using Machine Learning in Python6. Food and SportsMachine Learning is being used in many everyday areas like food quality testing and sports analysis. It can predict wine quality, estimate calories burned, forecast insurance costs and even predict cricket match scores, helping people make better decisions in daily life.Wine Quality PredictionIPL Score Prediction Using Deep LearningCalories Burnt Prediction using Machine Learning7. Transportation, Traffic and EnvironmentTransport systems and the environment generate large amounts of data. Machine Learning can study this data to improve traffic planning, forecast ride demands and even predict rainfall to help in agriculture and disaster management.Vehicle Count Prediction From Sensor DataOla Bike Ride Request Forecast using MLRainfall Prediction using Machine Learning in Python8. Other Important Machine Learning ProjectsMachine Learning can also be used in many other areas like detecting fake news, predicting tips at restaurants or forecasting product demand. These projects explore unique and practical uses of ML.Human Scream Detection and Analysis for Controlling Crime RateSpaceship Titanic Project using Machine Learning in PythonInventory Demand Forecasting using Machine Learning in PythonWaiter’s Tip Prediction using Machine LearningFake News Detection using Machine LearningFake News Detection Model using TensorFlow in PythonPredict Fuel Efficiency Using Tensorflow in PythonAdvanced Machine Learning Projects With Source Code Here we have discussed a variety of complex machine-learning projects that will challenge both your practical engineering skills and your theoretical knowledge of machine learning.1. Image and Video ProcessingMachine Learning is very powerful in working with pictures and videos. These projects include things like detecting faces, identifying diseases from X-rays, classifying animals and recognizing traffic signs.Multiclass image classification using Transfer learningImage Caption Generator using Deep Learning on Flickr8K datasetFaceMask Detection using TensorFlow in PythonDog Breed Classification using Transfer LearningFlower Recognition Using Convolutional Neural NetworkCat & Dog Classification using Convolutional Neural Network in PythonTraffic Signs Recognition using CNN and Keras in PythonResidual Networks (ResNet) – Deep LearningLung Cancer Detection using Convolutional Neural Network (CNN)Lung Cancer Detection Using Transfer LearningBlack and white image colorization with OpenCV and Deep LearningPneumonia Detection using Deep LearningDetecting Covid-19 with Chest X-rayDetecting COVID-19 From Chest X-Ray Images using CNNImage Segmentation Using TensorFlow2. Recommendation SystemsRecommendation systems suggest what you might like to watch, listen to or buy. These projects show how ML can recommend movies, music or talks based on your preferences.Ted Talks Recommendation System with Machine LearningPython | Implementation of Movie Recommender SystemMovie recommendation based on emotion in PythonMusic Recommendation System Using Machine Learning3. Speech and Language ProcessingWith Machine Learning, computers can understand and process human language. Projects like speech recognition, chatbots and sentiment analysis show how ML makes communication with machines easier.Speech Recognition in Python using Google Speech APIVoice Assistant using pythonNext Sentence Prediction using BERTHate Speech Detection using Deep LearningFine-tuning the BERT model for Sentiment AnalysisSentiment Classification Using BERTSentiment Analysis with Recurrent Neural Networks (RNN)Autocorrect Feature Using NLP In PythonPython | NLP analysis of Restaurant reviewsRestaurant Review Analysis Using NLP and SQLite4. Health and Medical ApplicationsAdvanced ML can help doctors by detecting diseases like skin cancer or heart problems. These projects show how technology can support healthcare professionals.Skin Cancer Detection using TensorFlowHeart Disease Prediction using ANN5. Security and SurveillanceMachine Learning is also used in safety and security. Projects like intrusion detection and license plate recognition help in crime prevention and monitoring.Intrusion Detection System Using Machine Learning AlgorithmsLicense Plate Recognition with OpenCV and Tesseract OCRDetect and Recognize Car License Plate from a video in real-time6. Other Advanced Machine Learning ProjectsSome projects focus on exciting new areas like predicting a person’s age, tracking body movements or recognizing daily activities. These show the wide range of ML applications.Age Detection using Deep Learning in OpenCVFace and Hand Landmarks Detection using PythonHuman Activity Recognition – Using Deep Learning ModelHow can Tensorflow be used with the abalone dataset to build a sequential model? Comment More infoAdvertise with us K kumar_satyam Follow Improve Article Tags : Machine Learning AI-ML-DS Machine Learning Projects Tutorials Data Science Proejcts Data Science +2 More 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. It is used in various applications like image recognition, speech processing, language translation, 8 min read Types of Machine LearningMachine learning is the branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data and improve from previous experience without being explicitly programmed for every task.In simple words, ML teaches the systems to think and understand like h 13 min read What is Machine Learning Pipeline?In artificial intelligence, developing a successful machine learning model involves more than selecting the best algorithm; it requires effective data management, training, and deployment in an organized manner. A machine learning pipeline becomes crucial in this situation. A machine learning pipeli 7 min read Applications of Machine LearningMachine Learning (ML) is one of the most significant advancements in the field of technology. It gives machines the ability to learn from data and improve over time without being explicitly programmed. ML models identify patterns from data and use them to make predictions or decisions.Organizations 3 min read Python for Machine LearningMachine Learning with Python TutorialPython language is widely used in Machine Learning because it provides libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. These libraries offer tools and functions essential for data manipulation, analysis, and building machine learning models. It is well-known for its readability an 5 min read Pandas TutorialPandas (stands for Python Data Analysis) is an open-source software library designed for data manipulation and analysis. Revolves around two primary Data structures: Series (1D) and DataFrame (2D)Built on top of NumPy, efficiently manages large datasets, offering tools for data cleaning, transformat 6 min read NumPy Tutorial - Python LibraryNumPy is a core Python library for numerical computing, built for handling large arrays and matrices efficiently.ndarray object – Stores homogeneous data in n-dimensional arrays for fast processing.Vectorized operations – Perform element-wise calculations without explicit loops.Broadcasting – Apply 3 min read Scikit Learn TutorialScikit-learn (also known as sklearn) is a widely-used open-source Python library for machine learning. It builds on other scientific libraries like NumPy, SciPy and Matplotlib to provide efficient tools for predictive data analysis and data mining.It offers a consistent and simple interface for a ra 3 min read ML | Data Preprocessing in PythonData preprocessing is a important step in the data science transforming raw data into a clean structured format for analysis. It involves tasks like handling missing values, normalizing data and encoding variables. Mastering preprocessing in Python ensures reliable insights for accurate predictions 6 min read EDA - Exploratory Data Analysis in PythonExploratory Data Analysis (EDA) is a important step in data analysis which focuses on understanding patterns, trends and relationships through statistical tools and visualizations. Python offers various libraries like pandas, numPy, matplotlib, seaborn and plotly which enables effective exploration 6 min read Feature EngineeringWhat is Feature Engineering?Feature engineering is the process of turning raw data into useful features that help improve the performance of machine learning models. It includes choosing, creating and adjusting data attributes to make the model’s predictions more accurate. The goal is to make the model better by providing rele 5 min read Introduction to Dimensionality ReductionWhen working with machine learning models, datasets with too many features can cause issues like slow computation and overfitting. Dimensionality reduction helps to reduce the number of features while retaining key information. Techniques like principal component analysis (PCA), singular value decom 4 min read Feature Selection Techniques in Machine LearningIn data science many times we encounter vast of features present in a dataset. But it is not necessary all features contribute equally in prediction that's where feature selection comes. It involves selecting a subset of relevant features from the original feature set to reduce the feature space whi 5 min read Feature Engineering: Scaling, Normalization, and StandardizationFeature Scaling is a technique to standardize the independent features present in the data. It is performed during the data pre-processing to handle highly varying values. If feature scaling is not done then machine learning algorithm tends to use greater values as higher and consider smaller values 6 min read Supervised LearningSupervised Machine LearningSupervised machine learning is a fundamental approach for machine learning and artificial intelligence. It involves training a model using labeled data, where each input comes with a corresponding correct output. The process is like a teacher guiding a student—hence the term "supervised" learning. I 12 min read Linear Regression in Machine learningLinear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, mea 15+ min read Logistic Regression in Machine LearningLogistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class. It is used for binary classification where the output can be one of two po 11 min read Decision Tree in Machine LearningA decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root node, branches, internal nodes and leaf nodes. It It works like a flowchart help to make decisions step by step where: Internal nodes re 9 min read Random Forest Algorithm in Machine LearningRandom Forest is a machine learning algorithm that uses many decision trees to make better predictions. Each tree looks at different random parts of the data and their results are combined by voting for classification or averaging for regression. This helps in improving accuracy and reducing errors. 5 min read K-Nearest Neighbor(KNN) AlgorithmK-Nearest Neighbors (KNN) is a supervised machine learning algorithm generally used for classification but can also be used for regression tasks. It works by finding the "k" closest data points (neighbors) to a given input and makesa predictions based on the majority class (for classification) or th 8 min read Support Vector Machine (SVM) AlgorithmSupport Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. It is useful when you want to do binary classification like spam vs. not spam or 9 min read Naive Bayes ClassifiersNaive Bayes is a classification algorithm that uses probability to predict which category a data point belongs to, assuming that all features are unrelated. This article will give you an overview as well as more advanced use and implementation of Naive Bayes in machine learning. Illustration behind 7 min read Unsupervised LearningWhat is Unsupervised Learning?Unsupervised learning is a branch of machine learning that deals with unlabeled data. Unlike supervised learning, where the data is labeled with a specific category or outcome, unsupervised learning algorithms are tasked with finding patterns and relationships within the data without any prior knowl 8 min read K means Clustering – IntroductionK-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ 4 min read Hierarchical Clustering in Machine LearningHierarchical clustering is used to group similar data points together based on their similarity creating a hierarchy or tree-like structure. The key idea is to begin with each data point as its own separate cluster and then progressively merge or split them based on their similarity. Lets understand 7 min read DBSCAN Clustering in ML - Density based clusteringDBSCAN is a density-based clustering algorithm that groups data points that are closely packed together and marks outliers as noise based on their density in the feature space. It identifies clusters as dense regions in the data space separated by areas of lower density. Unlike K-Means or hierarchic 6 min read Apriori AlgorithmApriori Algorithm is a basic method used in data analysis to find groups of items that often appear together in large sets of data. It helps to discover useful patterns or rules about how items are related which is particularly valuable in market basket analysis. Like in a grocery store if many cust 6 min read Frequent Pattern Growth AlgorithmThe FP-Growth (Frequent Pattern Growth) algorithm efficiently mines frequent itemsets from large transactional datasets. Unlike the Apriori algorithm which suffers from high computational cost due to candidate generation and multiple database scans. FP-Growth avoids these inefficiencies by compressi 5 min read ECLAT Algorithm - MLECLAT stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It is a data mining algorithm used to find frequent itemsets in a dataset. These frequent itemsets are then used to create association rules which helps to identify patterns in data. It is an improved alternative to the A 3 min read Principal Component Analysis(PCA)PCA (Principal Component Analysis) is a dimensionality reduction technique used in data analysis and machine learning. It helps you to reduce the number of features in a dataset while keeping the most important information. It changes your original features into new features these new features don’t 7 min read Model Evaluation and TuningEvaluation Metrics in Machine LearningWhen building machine learning models, it’s important to understand how well they perform. Evaluation metrics help us to measure the effectiveness of our models. Whether we are solving a classification problem, predicting continuous values or clustering data, selecting the right evaluation metric al 9 min read Regularization in Machine LearningRegularization is an important technique in machine learning that helps to improve model accuracy by preventing overfitting which happens when a model learns the training data too well including noise and outliers and perform poor on new data. By adding a penalty for complexity it helps simpler mode 7 min read Cross Validation in Machine LearningCross-validation is a technique used to check how well a machine learning model performs on unseen data. It splits the data into several parts, trains the model on some parts and tests it on the remaining part repeating this process multiple times. Finally the results from each validation step are a 7 min read Hyperparameter TuningHyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters. These are typically set before the actual training process begins and control aspects of the learning process itself. They influence the model's performance its complexity and how fas 7 min read ML | Underfitting and OverfittingMachine learning models aim to perform well on both training data and new, unseen data and is considered "good" if:It learns patterns effectively from the training data.It generalizes well to new, unseen data.It avoids memorizing the training data (overfitting) or failing to capture relevant pattern 5 min read Bias and Variance in Machine LearningThere are various ways to evaluate a machine-learning model. We can use MSE (Mean Squared Error) for Regression; Precision, Recall, and ROC (Receiver operating characteristics) for a Classification Problem along with Absolute Error. In a similar way, Bias and Variance help us in parameter tuning and 10 min read Advance Machine Learning TechniqueReinforcement LearningReinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize cumulative rewards. RL allows machines to learn by interacting with an environment and receiving feedback based on their actions. This feedback comes 6 min read Semi-Supervised Learning in MLToday's Machine Learning algorithms can be broadly classified into three categories, Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Casting Reinforced Learning aside, the primary two categories of Machine Learning problems are Supervised and Unsupervised Learning. The basic 4 min read Self-Supervised Learning (SSL)In this article, we will learn a major type of machine learning model which is Self-Supervised Learning Algorithms. Usage of these algorithms has increased widely in the past times as the sizes of the model have increased up to billions of parameters and hence require a huge corpus of data to train 8 min read Ensemble LearningEnsemble learning is a method where we use many small models instead of just one. Each of these models may not be very strong on its own, but when we put their results together, we get a better and more accurate answer. It's like asking a group of people for advice instead of just one person—each on 8 min read Machine Learning PracticeTop 50+ Machine Learning Interview Questions and AnswersMachine Learning involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. Machine Learning is one of the booming careers in the present-day scenario.If you are preparing for machine learning interview, this intervie 15+ min read 100+ Machine Learning Projects with Source Code [2025]This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read Like