Top 20 Machine Learning Projects with Source Code for 2024

Stock Prices Predictor

Here's an exciting machine learning project idea for data scientists and machine learning engineers interested in the finance domain. A stock price predictor is a system that learns about a company's performance and predicts future stock prices. The challenge with stock price data is its granularity, and it involves various data types, including volatility indices, prices, global macroeconomic indicators, and fundamental indicators. A benefit of working with stock market data is the shorter feedback cycles in financial markets, allowing data experts to validate their predictions more quickly. To start, consider a simple machine learning problem like predicting 6-month price movements based on fundamental indicators from an organization's quarterly report. Stock market datasets can be downloaded from Quandl.com or Quantopian.com. There are various time series forecasting methods available to forecast stock prices, demand, and more.

Project Idea: A time series analyzes event occurrences over time to identify patterns and predict future events based on trends. This method is excellent for understanding seasonal variations, identifying repetitive patterns, and detecting unexpected events to investigate their causes further. Various models can be used for time series forecasting, with the choice depending on factors such as the availability of historical data, the forecast context, the required forecast period, and the time available to develop and apply the model.

Some common models for time series forecasting include:

• Moving Average: A straightforward technique that predicts the next occurrence as the mean of all past occurrences. Despite its simplicity, it is often quite accurate.

• Exponential Smoothing: This method calculates the mean by giving less weight to older occurrences and more to recent ones, making it more responsive to recent changes.

• ARIMA (Autoregressive Integrated Moving Average): A more complex model that uses regression analysis to monitor the strength of a dependent variable based on other changing variables.

Check out the source code to determine which forecasting method to use and how to apply it with time series forecasting examples.

Industry: Finance

Iris Flowers Classification

One of the simplest machine learning projects involves using the Iris Flowers dataset, widely recognized as the "Hello World" of machine learning. This dataset is a classic in classification problems literature and is ideal for beginners. It features numeric attributes, making it easy to load and handle without requiring complex transformations or scaling. The Iris dataset is small, fits easily into memory, and is straightforward to work with.

Project Idea: Download the Iris Dataset from the UCI ML Repository. The goal of this beginner-friendly data science project is to classify the flowers into three species—Virginica, Setosa, or Versicolor—based on the length and width of the petals and sepals. By implementing advanced algorithms, you can enhance this project and add it to your deep learning projects portfolio.

Industry: Medicine

House Pricing Prediction

The Boston House Prices Dataset contains housing prices from various locations in Boston, along with information on non-retail business areas (INDUS), crime rate (CRIM), age of house owners (AGE), and several other attributes, totaling 14.

Project Idea: Download the Boston Housing dataset from the UCI Machine Learning Repository. The goal of this project is to predict the selling price of a new home by applying basic machine learning concepts to the housing price data. With 506 observations, this dataset is small but ideal for beginners to practice regression concepts. Additionally, those new to deep learning can use this dataset to experiment with deep learning algorithms.

Industry: Real Estate

Source Code: Housing Price Prediction

Loan Eligibility Prediction

Loans are essential for economic growth, providing individuals and businesses the capital needed to invest and take risks. Banks, which earn profit primarily from interest on loans, follow a rigorous process to approve loans. Predicting loan eligibility can help streamline planning and decision-making.

Project Idea: Train a model to predict loan eligibility using a dataset from SYL Bank, one of Australia’s largest banks. This dataset includes information such as sex, marital status, number of dependents, income, qualifications, credit card history, and loan amount. The project involves using cross-validation for training and testing the model. Start with data visualization techniques to clean the data and fill in missing values. This project provides an excellent opportunity to learn how to build statistical models like Gradient Boosting and XGBoost and understand metrics such as the ROC Curve and MCC scorer.

Industry: Financial Services

Source Code: Loan Prediction Analysis

Music Recommendation System

This project is one of the most popular machine learning projects and can be applied across various domains. If you've used an e-commerce site or a movie/music streaming service, you're likely familiar with recommendation systems. For instance, Amazon recommends products to add to your cart, and Netflix or Spotify suggests movies or songs based on your preferences. These systems exemplify the application of machine learning.

Project Idea: In this project, we will use a dataset from Asia's leading music streaming service to build an improved music recommendation system. The goal is to predict which new songs or artists a listener might like based on their previous choices. The primary task is to predict the likelihood of a user listening to a song repeatedly within a specific time frame. The dataset indicates whether a user has listened to the same song within a month. It includes information about which song was heard by which user and at what time. Use classification machine learning algorithms to address this problem, and for an additional challenge, experiment with deep learning algorithms like neural networks.

Industry: Entertainment

Source Code: Music Recommendation Machine Learning Project