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10 Machine Learning Projects to Get You Hired in 2021

Machine Learning, ML, is a technology with a massive scope of implementation. You can use ML techniques to develop the codes and algorithms to automate a basic to an advanced process. Machine Learning is applicable in a broad range of business and industrial domains. 

Machine Learning skills will open new vistas for you as it is one of the high-on demand technologies. You will get to develop an understanding of ML with the project ideas in the list below. You can explore these ideas to acquire and improve your skills in Machine Learning technologies with hands-on experience on the same. 

Machine Learning Projects to Practice in 2021

1. Retail Price Optimization Project 

You can develop a dynamic pricing model using regression trees as the ML technology. Retail prices do not remain static, and it is essential to optimize the costs as per the customer demands constantly.  You can take the data of a restaurant or an e-commerce website to optimize the price of specific items according to the price elasticity. We recommend developing the codes in Python for unmatched usability and robustness. 

2. E-commerce Product Reviews – Sentiment Analysis 

E-commerce is something that is in use by a majority of the population across the globe. As a customer, there is often confusion in the selection of a suitable e-commerce retailer. 

You can resolve such concerns by developing an ML-based sentiment analysis system for e-commerce channels. It will analyse the dataset from the product reviews by the customers. Language data processing or filtering is the first step in this project, followed by feature extraction, pairwise review ranking and classification.

3. House Price Prediction Model 

This is one of the most beginner level basic ML project wherein you can use the Zillow Zestimate dataset from Kaggle. You can develop a regression ML model to predict the prices of a house as per the location, land size, and other variables. 

The project will allow you to tune the hyperparameters of the models to attain optimal performance. You can perform cross-validation to assure the quality of the algorithms. 

4. Fire Detection using Image Segmentation 

The project is a deep learning project and uses Mask R-CNN with Tensorflow. You can use Python as the programming language to work on this project idea. 

The project will perform fire detection by adopting an RGB model. The model will measure chromatic and disorder and extract the fire and smoke pixels for detection. 

5. Music Recommendation Algorithm

Many popular music streaming applications, such as Spotify or Amazon Music, provide recommendations to the users. These recommendations are per the browsing and music history of the users. You can also develop a similar application using Machine Learning techniques. 

The project uses customer data to predict the chances of a user listening to a particular song or album. You can use the KKBOX dataset to develop an ML-based music recommendation algorithm. 

6. Loan Default Risk Prediction Project 

For this project, you can use R or Python to develop the ML code. Banks and financial institutions face the risks of fraudulent practices and loan defaulters. 

You can develop an ML-based loan default risk prediction project to determine the repayment capabilities of the customers. Financial institutions can provide a safe borrowing experience to their customers with such an automated system for predicting load defaults. 

7. Collaborative Filtering Recommendation System 

This project involves the use of cosine distances and nearest neighbour algorithms. You can apply collaborative filtering to design an ML-based recommendation system. 

This project will apply to any of the e-commerce/online streaming websites or apps. Amazon reviews dataset is the most suitable for this project as it will provide you with over 2 million records. 

8. Wine Quality Prediction 

In this project, you can use ML models to come up with a wine quality prediction system. You can use Python as the programming language to work on this project and can apply logistic regression for predictions. A non-linear decision tree is also a good choice to carry out the predictions. 

For this project, you may target the wine experts or the customers as your primary end-users. Combining the objective tests and the sensory data can provide predictions on the wine quality for the wine experts. 

9. Production Line Performance Project 

You can apply and implement machine learning techniques and models to develop a production line performance project for any organisation of your choice. 

Bosch, for example, has multiple assembly lines and faces a significant probability of internal failures. You can gather thousands of measurement and test records forvariouse components across the assembly lines. Random Forest Classifier is the apt model for training in this case. The probabilistic model can also be a good choice for training. 

The combination of correlation and Violin plot can provide the best features for extraction and prediction. 

10. Resume Parsing with Machine Learning 

Recruiters have a tough time screening the applications for a particular job vacancy. You can simplify the task with Machine Learning and can develop an automated resume parser. 

Spacy for OCR and text classifications is a popular Python library and will be adequate to work on this project. The project will include implementing Natural Language Processing (NLP) techniques to pick out the specific fields from the resumes. 

These projects will enable you to work on several Machine Learning techniques and models, such as deep learning, neural networks, classification, and more. All of the project ideas have real-world applications and implementations. You can work on these projects to learn ML techniques and improve your programming and development skills in ML. You will also face certain complexities while working on the projects and enhance your problem-solving and analytical abilities. These basic projects will introduce you to Machine Learning models, and you can then expand and implement your skills to work on advanced and complex ML codes and algorithms.

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