Top 6 Machine Learning Project Ideas for AI ML Beginners

In many instances, while dealing with real-world data, the data will be imbalanced i.e., the number of observations per class is not equally distributed. The machine learning models will struggle to predict the minority class. So, we must balance the data. Credit card fraud detection is a classic imbalanced data problem, the occurrence of fraud is uncommon. we will have a large number of normal transactions with a minuscule number of fraud incidents. Tackling this kind of imbalanced data will give you experience when you come across similar kinds of data in real-world applications. There are usually three common methods employed to reduce the imbalance

1. Under-sampling
2. Oversampling
3. combination of the above two

The performance of the model before and after balancing the data will give you a good idea how different algorithms respond to an imbalance in data.

It is one of the classic datasets, it contains various features of houses in Boston City such as numbers of rooms, crime rate, accessibility to highway, tax rate, etc. We use these various features to determine the price of the house. We can perform Exploratory data analysis on this dataset and also understand the relationship between various features. Having so many features we can’t use all the features in our final model. We can employ some basic methods like Principal component analysis to reduce the number of features. We can learn how the removal and addition of a feature can affect the accuracy of the model.

Sentiment Analysis is a subset of Natural Language processing, where we try to find if a particular review, tweet, article, etc. have a negative, positive or neutral sentiment. Sentiment analysis has a wide range of applications, it can be used to understand the emotions, opinions, and attitudes of a particular or a group of persons toward a product, service, etc. After mining data from Twitter, we will need to broadly follow these four steps

1. Cleaning data Removing the stop words (“is”, “a”, “the”, etc.), Remove special characters.
2. Normalization converting uppercase to lowercase, Numeric words to numbers
3. Tokenization Converting the tweets into smaller components I.e., words.
4. Vectorization Assign values to each of the words

After these four steps, we will have in our hand a bunch of numbers with each row representing a tweet. We can use different types of algorithms to build our model decision tree, support vector machines, neural networks, etc.

This is one of the Basic introductory projects in Machine learning. We need to classify different species of Iris flowers based on their length and width of sepals and petals. There are three species in Iris flowers. Each having its properties, which we will harness to classify. We can also experiment with some clustering algorithms. We can compare how the clustering algorithm with stack up against the classification algorithm. The time taken for both the algorithm to converge and we can also compare how their accuracy is affected with different ratios of train and test data.

It involves detection and estimation of human pose.  This wide topic has a lot of scope like helping in training humanoid robots, Motion Tracking for Consoles, Activity Recognition, etc.

We will be discussing one of the most popular Human Pose Estimation OpenPose. It follows a bottom-up approach whereas it detects all the key parts (legs, arms, torso) in the image and then assigns them to distinct individuals. OpenPose network uses Deep learning for detection and assignment. It works step by step as follows.

1. Part Confidence Maps this layer generates a map representing a particular part of the human pose skeleton.
2. Part Affinity Field this layer generates values that represent the degree of association between different parts.
3. Bipartite Matching bipartite graphs are formed between pairs of parts.

The less significant values in bipartite graphs are removed. In the end, we get a stick graph that shows the parts in a circle and sticks connecting the limbs.

The stock market is filled with data, it is an excellent project for beginners in AI ML. The data can be easily obtained and mostly there won’t be much preprocessing required. We can also track the live performance of our algorithm and tune it further according to the results. Different stock markets can also be used to improve prediction. The Time Series Analysis can be used to study the stock market data by using the various components of the Time Series Analysis

• Trend The continuous pattern of the time series can be an increasing or decreasing pattern. If our time series does not exhibit any increasing or decreasing pattern then it is a stationary series.
• Cyclicity Any pattern showing an up and down movement around a given trend is identified as a cyclical pattern. The duration of the cycle depends on the type of industry or business being analyzed.
• Seasonality is the component of the time series which repeats over a certain period (daily, yearly, etc.)
• Irregularity is the component of the time series which is unpredictable.

Final Thoughts

As we come to the end of the article, I would like to say that there are different ways to do AIML projects, either my own or taking up some AIML certification courses which help you to do capstone projects with the help of faculties from top universities. So, now that it is on your plate to decide which is the best way to do projects and get into a dream job!