Machine learning is the most widely used technology that enables machines to become more intelligent. One of the best ways to learn more about machine learning is through working on real-life projects. This article guides you with a list of machine learning projects for beginners, intermediates, and experts with source code to acquire in-depth knowledge and explore real-time datasets.
Machine learning projects for beginners
1. House Price Prediction
In this project, you can learn to predict the prices of houses by using the dataset with the XGBoost and the Linear regression algorithm. The dataset contains information about the house’s location, the house’s price, the house, square feet of the house, and more.
Link to the project: House Price Prediction
2. Music Recommendation System
One of the most famous music apps, Spotify, always shows music that you may like. This app works by using machine learning algorithms. In this project, you will initially predict the possibility of the user listening to a song on a loop within a time frame.
When you talk about the recommendation system, there are mainly two types of recommendation systems, i.e., Content-based filter recommendation systems and Collaborative filter recommendation systems. Content-based filter systems give you a recommendation based on the similarity of the two songs’ contents or attributes. The collaborative-based filter predicts possible references using a matrix with ratings on different songs.
In this project, you can generate a content-based music recommendation system that uses a dataset of names, artists, and lyrics of 57650 songs in English obtained from Kaggle. This project also creates a collaborative filtering music recommendation system using the Million Song Dataset, which has a freely available collection of audio features and metadata for a million contemporary popular songs.
Link to the project: Music Recommendation System
3. Loan Prediction using Machine Learning
With this project, you can build a machine-learning model that will help to analyze how much loan the user can take. Different machine learning models like logistic regression, decision trees, random forest, and XGBoost are implemented in this project. The dataset used in this project consists of user information such as marital status, education, number of dependents, employment, and more.
Link to the project: Loan Prediction using Machine Learning
4. Iris Flowers Classification ML Project
The Iris flower dataset is the most widely used dataset for classification. It consists of three different flowers, such as Setosa, Virginia, and Versicolor, and each flower contains features like Sepal width, Sepal length, Petal length, and Petal width.
In this project, you can learn to train the Support vector machine and supervised machine learning models with the iris flower dataset. While implementing this project, you can explore more about machine learning, data visualization, model creation, data analysis, and more.
Link to the project: Iris Flowers Classification ML Project
5. Wine Quality Prediction
The wine quality prediction project is to build a machine-learning model to detect the quality of wines by using different chemical properties in the wine dataset. The wine dataset consists of 4898 observations with one dependent and eleven independent variables.
In this project, each wine in the dataset is given a quality score between 0 to 10. As a result, the output of this project is calculated as good when the score of wine is seven or greater than seven. The output is treated as bad-quality wine when the score is below seven. The wine dataset includes attributes like fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulfates, and alcohol. With this project, you learn to apply different classification methods to check the quality of wine and identify which attributes contribute more to detecting the quality of wines.
Link to the project: Wine Quality Prediction
Machine learning projects for intermediates
1. Market Basket Analysis
In this project, you learn to conduct a Market Basket analysis to predict consumer purchasing behaviors. The output of this project is split into three sections. The first section is to source, explore, and format a complex dataset suitable for modeling with recommendation algorithms. In the second part, you can apply various machine-learning algorithms for product recommendation and select the best-performing model with the help of the ‘recommenderlab’ package.
You can implement the selected model in a Shiny Web Application in the final stage. The online retail dataset consists of transactions occurring between 1st Dec 2010 and 9th Dec 2011 for a UK-based and registered online retail company. As per the principles of Market Basket Analysis, if a consumer purchases a group of items, that consumer is likely to purchase similar items as well. This complete idea is implemented in the Market Basket Analysis project.
Link to the project: Market Basket Analysis
2. Text Summarization
In this project, the text summarization is performed while conserving its meaning. In this project, extractive text summarization employs a scoring function for recognizing and picking essential pieces of text from documents and compiling them into an edited version of the original text.
Abstractive text summarization uses high-level natural language processing techniques to build a new, shorter version that conveys the same information. For the implementation of this project, and you need to know about essential Python libraries like Numpy, Pandas, and NLTK.
Link to the project: Text Summarization
3. Black Friday Sales Prediction
Black Friday is celebrated on Friday following Thanksgiving Day in the United States on the 4th Thursday of November. The day after Thanksgiving is regarded as the start of the United States Christmas shopping season. Many stores offer highly promoted sales on Black Friday and open very early, like at midnight. The major challenge for stores or e-commerce businesses is to select the product price such that they gain maximum profit at the end of sales. This project determines the product price with the help of historical retail store sales data.
The dataset in this project is taken from the online analytics hackathon hosted by Analytics Vidhya. It consists of attributes such as marital status, gender, product categories, purchase amount, and city demographics. The dataset contains 12 columns and 537577 records.
Different supervised machine learning models like Linear Regression, Decision Tree, Random Forest, and XGBoost algorithms are implemented in this project. RMSE is used in this project as a way to measure the errors in the machine learning models.
Link to the project: Black Friday Sales Prediction
Machine learning projects for experts
1. BigMart Sales Prediction ML Project
Data scientists suggest learning different machine learning projects to diversify your knowledge. Therefore, this project will help you with different unsupervised machine learning algorithms by using the sales dataset of a grocery supermarket store.
BigMart sales dataset contains sales data for 2013 for 1559 products across ten different outlets in different cities. This project aims to build a regression model to predict sales of 1559 products for the following year in each of the ten different BigMart outlets. The BigMart sales dataset also contains specific attributes for each product and store. The machine learning model helps BigMart understand the properties of products and stores that play an essential role in increasing their sales.
Link to the project: BigMart Sale Prediction ML Project
2. Sales Forecasting using the Walmart dataset
Sales forecasting is one of the most common use cases of machine learning to identify factors that affect product sales and estimate future sales volume. This project uses the Walmart dataset of sales data for 98 products across 45 outlets. The dataset contains sales per store and department weekly.
This project aims to forecast sales for every department in each outlet to help them make good data-data-driven channel optimization and inventory planning. While working with the Walmart dataset, one challenge is that it consists of markdown events affecting sales. With this machine learning project, you can build the predictive model using the Walmart dataset to estimate the number of sales they will make in the future.
Link to the project: Sales Forecasting using Walmart Dataset
