Working on Machine Learning Projects is what sets you apart from a regular Machine Learning student.

More and more students are enrolling in machine learning courses and finding solutions online. 

However, cracking machine learning is not limited to enrolling for classes, taking notes, appearing for tests and getting a certification. It is crucial to put all you’ve learned into practice, and we mean real-world practice!

You see the human mind is nothing like a machine. We don’t have fancy algorithms which learn once and never forget! We need to brush up and perfect whatever we’ve learned through continuous practice.

Data Sources for your next ML project in 2020

These are some platforms from where you can source your data:

• Kaggle
  All things Google are the best, aren’t they? Google’s online platform, Kaggle, for data scientists and machine learning buffs. The vast online community is supportive and talented, learning as they communicate. You can find a variety of data-sets on Kaggle for your project and also abundant help with the same!
• UCI Repository
  UCI Repository collects databases, data generators and domain theories. The repository currently has around 474 data-sets to work on and has been trusted by machine learning students to explore data.
• Data.world
  Data.world is another open data source for budding Data Scientists. You can use the platform to source, copy, modify, analyze and download data to work on your own machine learning projects.

Here is a list of the top 10 projects in Machine Learning that you should focus on in 2020.

1. Housing Prices Prediction Project

The dataset includes house prices of the Boston residential areas. The prices of the houses vary according to different factors like crime rate, number of rooms, etc. Beginners can really benefit from this project to predict prices on the basis of new data.

Dataset: Housing Price Prediction Dataset

2. Stock Price Prediction Project

This Machine Learning project aims to predict the future price of the stock market based on the previous year’s data. However, the challenges in this project include stock prices data being granular, where they come in different types—volatility indices, rates, fundamental indicators. You will be predicting future stock price returns based on two sources of data: Market Data and News Data.

Dataset: Stock Price Prediction Dataset

3. Titanic Project

The Titanic Project is simple: all you have to do is deploy Machine Learning to create a model that predicts which passengers survived the Titanic shipwreck. The sinking of the Titanic is one of the most tragic shipwrecks in history, all you have to do is build a predictive model to predict which groups of people who were more likely to survive than others. 

Dataset: Titanic Dataset

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4. Uber Data Analysis Project

The dataset has information about 4.5 million Uber pickups in New York City from April 2014 to September 2014 and 14 million more from January 2015 to June 2015. Users can perform data analysis and gather insights from the data.

Dataset: Uber Project

5. Iris Flowers Classification Project

This is one of the most basic projects in Machine Learning. It’s a starter project for beginners. Dataset of Irish flowers has numeric attributes and does not require any pre-processing. The project requires you to classify the flowers into three species – virginica, setosa, or versicolor. 

Dataset: Iris Dataset

6. Credit Card Fraud Detection Project

Companies that involve heavy and frequent transactions with the use of credit cards need to be aware of frauds and anomalies. The project aims to build a model to detect frauds on credit cards. Historical transactions and their labels are marked as fraud or non-fraud to detect if new transactions made from the customer are fraudulent or not.

Dataset: Credit Card Fraud Dataset

7. Recommender Systems Dataset

This project lets you dabble with recommender systems that have been popular among YouTube, Netflix, Amazon users. You can use the dataset to build recommendation system like Amazon. A recommendation system can suggest favorite/preferable products, movies, etc. based on users’ interests and previous purchases.

Dataset: Recommender Systems Dataset

8. Color Detection Project

The dataset contains RGB values of 865 different colors. This dataset can be used to develop an app or model to identify colors from images. This can be used extensively in the use and editing of graphics.

Dataset: Color Detection Dataset

9. Sentiment Analysis Project

Sentiment analysis helps in analyzing the emotion and responses of the users. Responses can be categorized as positive, negative or neutral. It is a great project to understand how to perform sentiment analysis and it is widely being used nowadays.

Dataset: Sentiment analysis Dataset

10. Speech Emotion Recognition Project

This project uses audio data as input. It takes a part of speech as input and then determines in what emotions the speaker is speaking. The data can be used to identify different emotions like happy, sad, surprised, angry, etc.

Dataset: Speech Emotion Recognition Datase

These are the Top 10 Machine Learning Projects in 2020 you should be looking to attempt. If you are still not convinced why you should be taking up these projects, you should consider this.

Why should you care about Machine Learning projects?

1. You get to test the ML algorithms you learned all about in theory and controlled environments!
2. You develop a deeper understanding of data. As you explore your dataset, you are able to better identify with the problem presented to you in the first place.
3. Estimate relationships and patterns in your data before you deep dive.
4. Assess which algorithm is better suited where and understand the underlying reason.

Now that you know why you absolutely must fine-tune your skills using machine learning projects, get started!

Last thing to remember…

A rookie mistake people often commit in their machine learning projects is to overlook what their models are actually conveying. Incorrect models beget incorrect predictions or classifications. Let’s talk about two situations here:

• Overfit: Overfitting in machine learning models happens when the model is a tad too well trained for its own good. The model loses its ability to generalize. The model picks up the training data along with its noise, fluctuations and models on new, fresh datasets.
• Underfit: Underfit is also not ideal. Underfit models do not learn properly from the training data and consequently cannot apply their learning elsewhere. Underfit models fail to capture the relationship between the input and the output and you might need to restart with a different algorithm altogether.

Another solution to evaluating the model’s performance is cross-validation. Here, instead of dividing your dataset into training, validation and test data, you train the model on an entire dataset and test its performance on another, fresh dataset.

This si what you should keep in mind while working on different machine learning projects. Whatever is your dataset or machine learning course, working on these projects in 2020 will help you build a successful portfolio and further a successful career.

All the best!