Machine learning projects give practical insight into any technology you are working on. You can get all the knowledge about technology from study materials but working on real time projects will help you master that technology. These projects can be developed in Python, R or any other tools.
Machine Learning Projects for Beginners
In this article, we have provided ten machine learning project ideas for beginners which can give you real world experience of this growing technology.
Music Recommendation System Machine Learning Project
This is one of the most popular machine learning projects and can be used across different domains. You might have noticed that when you watch a movie on netflix or listen to a song in spotify, it shows similar movies or songs that you may like. Similarly several E commerce sites will suggest things you can add to your cart. You can build these recommendations using machine learning.
Use the dataset from Bollywood’s leading music streaming service to build a good music recommendation system. You can try to determine which new song or which new artist a listener might like based on their previous choices. The primary task is to predict the chances of a user listening to a song repetitively within a time frame. In the dataset, the prediction is marked as 1 if the user has listened to the same song within a month. The dataset consists of which song has been heard by which user and at what time.
Stock Prices Predictor using TimeSeries
This is an interesting machine learning project idea for data scientists/ machine learning engineers who are interested in the finance field. Stock prices predictor helps learn about the performance of a company and predicts it’s future stock prices. Time Series Forecasting uses information about historical market data and associated patterns to make a prediction for future activity. These patterns are concepts like trend analysis, cyclical fluctuation analysis, or issues of seasonality. There are different time series forecasting methods to forecast stock price, demand, etc. You can download Stock Market datasets from Quandl.com or Quantopian.com.
A time series helps to analyze the occurrences of events over a period of time. This helps to identify patterns to predict future occurrences based on trends. For example, you can use time series analysis to track the price of a security over a period of time. You can track the price over a short term, such as the price of a security over an hour during a business day, or for long term, such as the daily close price of a security over the course of five years.
You can use various models to perform time series forecasts. Various factors like availability of the past data, the context of the forecast, the time period for which the forecast has to be made, and the time available to create the model and make the forecast will help you select a model. Some of the models which can be used for time series forecasting are moving-average, exponential smoothing, and ARIMA (autoregressive integrated moving average) model. The moving average model is a very straightforward modeling technique that predicts the next occurrence to be the mean of all the past occurrences.
Iris Flowers Classification Project
Iris Flowers is the simplest machine learning datasets in classification literature. This is a basic project for machine learning beginners to predict the species of a new iris flower. This machine learning problem is often referred to as the “Hello World” of machine learning.
You can use this machine learning project is to classify the flowers among the three species – virginica, setosa, or versicolor. You can distinguish them based on the length and width of petals and sepals.
House Pricing Prediction Project
The dataset has the prices of houses across different places in Boston. It also consists of other information like areas of non-retail business (INDUS), crime rate (CRIM), age of people who own a house (AGE), and several other attributes.
This is a good project to predict prices of houses by applying basic machine learning concepts to the housing prices data. You can downloaded the dataset from the UCI Machine Learning Repository.
Inventory Demand Forecasting
Preparing sufficient inventory is an integral requirment of several outlets, especially those dealing with essential commodities. Businesses like restaurants and supermarkets have to ensure that they have enough stock to meet the customers’ needs. This will help them grow and also avoid unnecessary wastage of their resources.
These predictions in demand forecasting can be made through the application of relevant machine learning algorithms. This machine learning project can be implemented by utilizing machine learning algorithms like Bagging, Boosting, XGBoost, Gradient Boosting Machine (GBM), Support Vector Machines etc.
Customer Segmentation using Machine Learning
This will help companies to run user-specific campaigns and offers rather than broadcasting same offer to all the users.
Using the segmentation technique, you can divide the customers based on their purchase history, gender, age, interest, etc. This information will help stores personalize marketing and provide customers with relevant deals.
Zillow Home Value Prediction Machine Learning Project
Zestimate, introduced by Zillow, is a tool that provides the worth of a house based on various attributes like public data, sales data, etc. Zestimate has information of more than 97 million homes. It helps you analyze the worth of a house before renting or moving in.
In this Machine Learning project, you can use the Zillows Economics dataset to build a house price prediction model with XGBoost based on factors like average income, crime rate, number of hospitals, number of schools, etc. This project will help you answer questions like top states with highest rent values, Zestimate per square feet, the median rental price for all homes, etc.
Loan Eligibility Prediction
Loans are the core business for banks since their main profit comes from interest on loans. But they have to follow a rigorous process to approve a loan. Being able to predict the eligibility for a loan will be useful for banks as there can be better planning behind the loan being approved or rejected.
You can do the project using a dataset that consists of information including data such as sex, marital status, number of dependents, income, qualifications, credit card history etc. This project will require training and testing the data model using the method of cross validation.
Credit Risk Prediction Project
The aim of this machine learning project is to predict customers who will default on a loan or credit card payment. Banks may experience loss on the credit card product from various sources. One possible reason for the loss is when customers default on their debt preventing banks from collecting payments for the services rendered.
Examine the customer database to find out how many customers will be deligent in making payments in the next 2 years. There are various machine learning models for predicting which customers default on a loan so the banks can cancel credit lines for risky customers or decrease the credit limit on the card to minimize losses. These models will also help banks screen which customers can be approved for a credit card.
Emojify – Create Your Own Emoji
Emojis are an essential part of online chatting, product review, brand emotion etc. With deep learning, it is possible to detect human emotions from images.
Build a deep learning model to classify facial expressions from real images. Then you can map the classified emotion to an emoji or an avatar.
These machine learning projects will help enhance your applied skills. So, gear up and add these projects to your portfolio.