10 Most Impressive ML Projects for Your Portfolio

Showcase your ml skills in your portfolio with these awesome top 10 machine learning projects

Machine Learning, a subset of Artificial Intelligence, is a powerful technique that enables computers to learn from data and make predictions or judgments without being explicitly programmed. Developing Machine Learning skills is critical for anyone trying to establish a name for themselves in this profession. Working on Machine Learning Projects is an excellent method to enhance these talents. These projects provide hands-on experience and a deeper grasp of the methods and techniques utilized in Machine Learning. Here are 10 Machine Learning Projects to assist you in improving your skills and comprehension, as well as make your portfolio stand out in this competitive sector.

1. Breast Cancer Diagnosis:

The goal of this project is to classify breast tissue samples as benign or malignant based on biopsy data. The data comes from the UCI Breast Cancer Wisconsin dataset, which has 10 features and 569 instances. You can use different machine learning models, such as naive Bayes, neural networks, or random forests, to do binary classification on the data.

2. Classification of Iris:

The primary objective of this project is to identify the species of iris blooms (setosa, Versicolor, or Virginia) based on their petal and sepal measurements. The data comes from the UCI Iris dataset, which has 4 characteristics and 150 iris flower examples. To do multiclass classification on data, you can use various machine learning methods such as k-means clustering, linear discriminant analysis, or multilayer perceptron. You can also utilize techniques like visualization, correlation analysis, and cross-validation to investigate and validate your model.

3. MLOps Project for a Mask R-CNN on GCP using uWSGI Flask:

The goal of this project is to use a Mask R-CNN model, which is a cutting-edge deep learning model for object detection and segmentation, on the Google Cloud Platform with MLOps tools and techniques. You can use uWSGI and Flask to make a web application that can take an image as input and return the detected objects and their masks. You can also use tools like Docker, Kubernetes, and TensorFlow Serving to handle the deployment process and ensure scalability and reliability.

4. OpenCV Project for Beginners to Learn Computer Vision Basics:

This project entails studying the principles of computer vision using OpenCV, a prominent image processing and analysis toolkit. OpenCV may be used to accomplish a variety of tasks such as face detection, edge detection, color segmentation, feature extraction, and object recognition. OpenCV can also be used to create applications such as facial recognition, optical character recognition, and augmented reality.

5. Predictions of Stock Prices:

The course of action entails forecasting company stock prices using historical data and financial factors. Machine learning models such as ARIMA, LSTM, and GANs can be used to capture temporal relationships and volatility in data and provide accurate forecasts. To improve your predictions, you can also employ techniques like sentiment analysis, technical analysis, and anomaly identification.

6. Predictions of Wine Quality:

This project entails predicting wine quality using physicochemical variables such as acidity, alcohol concentration, pH, and so on. You can utilize the UCI Wine Quality dataset, which includes 12 attributes and 1599 red wine examples. To conduct regression or classification tasks on data, you can utilize machine learning models such as logistic regression, support vector machines, or gradient boosting. You can also enhance your model using approaches like feature selection, normalization, and regularization.

7. Recommender System Machine Learning Project for Beginners:

The goal of this project is to create a recommender system that can recommend movies to users based on their interests and ratings. To develop tailored recommendations, you can use the Movielens dataset, which has millions of user-generated movie reviews and several machine learning techniques such as collaborative filtering, matrix factorization, and deep learning. You can also use measures like precision, recall, and RMSE to assess the performance of your system.

8. Smartphone Human Activity Recognition:

The goal of this research is to recognize human movement using smartphone sensor data. The UCI HAR dataset, which comprises accelerometer and gyroscope values from 30 people engaged in six activities, can be used. To classify activities based on sensor data, you can use machine learning models such as decision trees, k-nearest neighbors, or convolutional neural networks. To increase the performance of your model, you can also employ techniques such as feature extraction, dimensionality reduction, and data augmentation.

9. Text Detection in Images using Python:

The purpose of this project is to recognize and extract text from photos using Python tools such as OpenCV and Tesseract. OpenCV can be used to preprocess images and pytesseract can be used to perform optical character recognition (OCR) on the images. To improve the quality and accuracy of text extraction, you can also employ techniques like contour detection, bounding box construction, and text cleaning.

10. Walmart's sales forecasting:

This project entails forecasting Walmart store sales based on past data and external factors such as holidays, weather, and promotions. Machine learning techniques such as linear regression, random forest, and neural networks can be used to capture patterns and trends in data and estimate future sales. To increase the accuracy of your predictions, you can also employ techniques like feature engineering, cross-validation, and hyperparameter tuning.