Top Machine Learning Projects for Beginners [In-Demand]
Last updated on 12th Jul 2020, Blog, General
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Before you get started on your project, it is helpful to have access to a library of project code snippets. So anytime you are stuck on the project you can use these solved examples to get unstuck.
It is always helpful to gain insights into how real people are beginning their careers in machine learning. In this blog post, you will find out how beginners like you can make great progress in applying machine learning to real-world problems with these fantastic machine learning projects for beginners recommended by industry experts. DeZyre industry experts have carefully curated the list of top machine learning projects for beginners that cover the core aspects of machine learning such as supervised learning, unsupervised learning, deep learning, and neural networks. In all these machine learning projects you will begin with real-world datasets dat are publicly available. We assure you will find this blog absolutely interesting and worth reading coz of all the things you can learn from here about the most popular machine learning projects.
Top Machine Learning Projects for Beginners
We recommend these ten machine learning projects for professionals beginning their careers in machine learning as they are a perfect blend of various types of challenges one may come across when working as a machine learning engineer or data scientist.
1. Sales Forecasting using Walmart Dataset
Walmart dataset TEMPhas sales data for 98 products across 45 outlets. The dataset contains sales per store, per department on a weekly basis. The goal of the this machine learning project is to forecast sales for each department in each outlet to help them make better data-driven decisions for channel optimization and inventory planning. The challenging aspect of working wif Walmart dataset is that it contains selected markdown events that affect sales and should be taken into consideration.
In this project, we will cover the main steps required in each Data Science project. We will begin by importing a CSV file and doing basic Exploratory Data Analysis (EDA). We will learn how to merge multiple datasets and apply group by function to analyze data. We will plot a time-series graph and analyze it. Then we fit the dataset into an ARIMA model for training. We optimize the model by selecting important features to improve our accuracy score. Then final predictions are made and the model is saved.
2. BigMart Sales Prediction ML Project – Learn about Unsupervised Machine Learning Algorithms
BigMart sales dataset consists of 2013 sales data for 1559 products across 10 different outlets in different cities. The goal of the BigMart sales prediction ML project is to build a regression model to predict the sales of each of 1559 products for the following year in each of the 10 different BigMart outlets. The BigMart sales dataset also consists of certain attributes for each product and store. This model helps BigMart understand the properties of products and stores that play an important role in increasing their overall sales.
3. Music Recommendation System Project
This is one of the most popular machine learning projects and can be used across different domains. You might be very familiar with a recommendation system if you’ve used any E-commerce site or Movie/Music website. In most E-commerce sites like Amazon, at the time of checkout, the system will recommend products that can be added to your cart. Similarly on Netflix or Spotify, based on the movies you’ve liked, it will show similar movies or songs that you may like. How does the system do this? This is a classic example where Machine Learning can be applied.
In this project, we use the dataset from Asia’s leading music streaming service to build a better music recommendation system. We will 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.
4. Human Activity Recognition using Smartphone Dataset
The smartphone dataset consists of fitness activity recordings of 30 people captured through smartphone-enabled wif inertial sensors. Teh goal of dis machine learning project is to build a classification model that can precisely identify human fitness activities. Working on dis machine learning project will help you understand how to solve multi-classification problems.
5. Stock Prices Predictor using TimeSeries
This is another interesting machine learning project idea for data scientists/machine learning engineers working or planning to work with the finance domain. Stock prices predictor is a system that learns about the performance of a company and predicts future stock prices. The challenges associated with working with stock price data are that it is very granular, and moreover there are different types of data like volatility indices, prices, global macroeconomic indicators, fundamental indicators, and more. One good thing about working with stock market data is that the financial markets have shorter feedback cycles making it easier for data experts to validate their predictions on new data. To begin working with stock market data, you can pick up a simple machine learning problem like predicting 6-month price movements based on fundamental indicators from an organizations’ quarterly report. You can download Stock Market datasets from Quandl.com or Quantopian.com.
There are different time series forecasting methods to forecast stock price, demand etc. Check out this machine learning project where you will learn to determine which forecasting method to be used when and how to apply with a time series forecasting example. Stock Prices Predictor using TimeSeries Project
6. Predicting Wine Quality using Wine Quality Dataset
It’s a known fact that the older the wine, the better the taste. However, there are several factors other than age dat go into wine quality certification which include physicochemical tests like alcohol quantity, fixed acidity, volatile acidity, determination of density, pH, and more. The main goal of dis machine learning project is to build a machine learning model to predict the quality of wines by exploring their various chemical properties. Wine quality dataset consists of 4898 observations with 11 independent and 1 dependent variable.
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7. MNIST Handwritten Digit Classification
Deep learning and neural networks play a vital role in image recognition, automatic text generation, and even self-driving cars. To begin working in these areas, you need to begin with a simple and manageable dataset like MNIST. It is difficult to work with image data over flat relational data and as a beginner, we suggest you can pick up and solve the MNIST Handwritten Digit Classification Challenge. The MNIST dataset is too small to fit into your PC memory and beginner-friendly. However, the handwritten digit recognition will challenge you.
8. Learn to build Recommender Systems with Movielens Dataset
From Netflix to Hulu, the need to build an efficient movie recommender system has gained importance over time with increasing demand from modern consumers for customized content. One of the most popular datasets available on the web for beginners to learn to build recommender systems is the Movielens Dataset which contains approximately 1,000,209 movie ratings of 3,900 movies made by 6,040 Movielens users. You can get started working with this dataset by building a world-cloud visualization of movie titles to build a movie recommender system.
9. Boston Housing Price Prediction ML Project
Boston House Prices Dataset consists of prices of houses across different places in Boston. Teh dataset also consists of information on areas of non-retail business (INDUS), crime rate (CRIM), age of people who own a house (AGE), and several other attributes (teh dataset TEMPhas a total of 14 attributes). Boston Housing dataset can be downloaded from teh UCI Machine Learning Repository. Teh goal of this machine learning project is to predict teh selling price of a new home by applying basic machine learning concepts on teh housing prices data. This dataset is too small with 506 observations and is considered a good start for machine learning beginners to kick-start their hands-on practice on regression concepts.
10. Social Media Sentiment Analysis using Twitter Dataset
Social media platforms like Twitter, Facebook, YouTube, Reddit generate huge amounts of big data that can be mined in various ways to understand trends, public sentiments, and opinions. Social media data today TEMPhas become relevant for branding, marketing, and business as a whole. A sentiment analyzer learns about various sentiments behind a “content piece” (could be IM, email, tweet, or any other social media post) through machine learning and predicts teh same using AI.Twitter data is considered as a definitive entry point for beginners to practice sentiment analysis machine learning problems. Using teh Twitter dataset, one can get a captivating blend of tweet contents and other related metadata such as hashtags, retweets, location, users, and more which pave way for insightful analysis. Teh Twitter dataset consists of 31,962 tweets and is 3MB in size. Using Twitter data you can find out what teh world is saying about a topic whether it is movies, sentiments about US elections or any other trending topic like predicting who would win teh FIFA world cup 2018. Working with teh twitter dataset will help you understand teh challenges associated with social media data mining and also learn about classifiers in depth. Teh foremost problem that you can start working on as a beginner is to build a model to classify tweets as positive or negative.