Fun Machine Learning Project for Beginners

Fun Machine Learning Project for Beginners


Fun machine learning project for beginners

As a beginner in machine learning, you might have read through machine learning concepts and decided to apply some of the concepts that you learnt there. Or you might just be a machine learning enthusiast wanting to have some fun and refresh some of your basics. Whatever be your level of knowledge, these fun machine learning projects for beginners will surely help you in improving your machine learning skills fast.

The projects below will ensure that your skills are challenged well enough to warm you up for the kind of problems you might face as an entrant to the world of machine learning. The data sets that will be used here are publicly available and drawn from real world sources. The projects will engage your skills from different machine learning topics such as supervised and unsupervised learning, deep learning and neural networks. Having completed these projects, you can make them part of your portfolio and use them to find awesome jobs and even negotiate for a higher salary!


1. Predicting stock prices


This interesting project in finance has crucial applications for numerous companies, as most of them are looking for ways to link their performance to the stock prices. It is therefore an exciting opportunity for data scientists looking towards working in the finance sector.

There is a variety of data that can be procured from stock markets, starting from stock prices to macroeconomic indicators, volatility indices, etc. Further, these trends also are constantly changing day by day, which will encourage creative thinking from your side to tackle the trading strategy.

Before starting however, a working knowledge in the following areas will help you with the extra oomph :

· Statistical modeling : It means translating real world data into mathematical equations while accounting for any uncertainties

· Predictive and regression analyses : You use techniques like data mining, data exploration, etc. to find out the behavior of possible implications. For this you find out the interaction between dependent and independent variable(s) and use it in your prediction.

· Action analyses : Here the actions from the above actions are analyzed and then the outcome is used in the machine learning process.

Tutorials :

Python: sklearn for Investing

R: Quantitative Trading with R

Data sources :

US Fundamentals Archive

Quantopian


2. Identifying default risk for home credit


Incomplete or absent credit histories lead to many people being duped by untrustworthy lenders. Such people face a perpetual struggle to get their loans approved. This project is aimed at providing the deserving people a chance for financial inclusion. To predict whether a client will be able to repay a particular loan or not, this project uses transaction information and other relevant data and various statistical methods and machine learning concepts.

The basic concepts that need to be mastered are supervised learning and classification.

Tutorials :

Supervised Learning with Python

Simple classification problem with Python

Data source:

Home credit


3. Predictor of sports matches


Outcome of sports matches like football and cricket are in huge contention and are often the subject of national pride as well as online betting. Machine learning is an interesting field that has been applied to this age old subject with good predictive accuracy.

The basic framework of machine learning can also be used for predicting the result of such systems. It also helps club managers decide the winning strategy to be used to move up the points table. Application of artificial neural network also helps in delivering highly accurate results.

The first thing to be created is a database of the sports under consideration, say for example, data from English Premier League football matches. The advanced parameters of the game need to be captured using json. This will also help in making more accurate predictions.

A working knowledge of Python will mean that you can easily use the tools available in Scikit like data mining, classifications and regression analysis. For best prediction results, human analysis tools like Vegas lines with some advanced parameters like Dean Oliver’s four factors can be used.

Data source:

English Premier League


4. Predicting house prices


The price of a house is often the most important parameter to decide whether it will be bought by a customer. But the price of a house can be decided by a number of factors which may not be limited just by the number of bedrooms or the availability of a gym in the neighbourhood.

This project aims at predicting the final price of each home. The beginner must have some basic skills in Python or R and know machine learning basics. The skill set of the data science students will be expanded here with the Boston Housing data set, which has been provided here to work on.

Skills required here are creative feature engineering and advanced regression techniques, for example, random forest. You need to identify which data is relevant and which is not. Based on the available variables a good prediction needs to be made on the price of the house, which will depend on multiple variables.

Data source:

Boston housing dataset


5. Predicting user movie ratings

Movie ratings help the average moviegoer decide whether a movie is worth watching. An aggregate of these ratings decides the average rating that a movie receives in popular review websites like Rotten Tomatoes and IMDb. The project challenges you to predict the rating a user might give to a movie, based on the ratings the user has given to other movies in the past. It also utilizes the information from similar users who have given ratings to similar movies.

You need to know basic machine learning algorithms like collaborative filtering and content-based filtering. Collaborative filtering will help make automated predictions based on the collected data from different users. Content based filtering will move this step ahead by suggesting items that are compared between the content of an item and the user’s profile.

Simple baseline methods, which are often used for predictions in a dataset, can be used here to find out the average ratings for the user generated reviews of any movie. Basically, you will model the relationship between the input data and the target variable and subsequently test its performance.

Data sources :

Netflix Prize

MovieLens Datasets


6. Sales forecasting


When a product is launched, the company needs to estimate its sales to determine the number of units to be produced. Will the new Maruti Suzuki WagonR sell more than 5 lakh units? If the price of Nescafe Classic is increased by 10 percent, how will the competition respond? What if marketing cost is cut by 30 percent?

Answer to questions above will be given by sales forecasting. It involves predicting the number of product units that will be sold under the current conditions of price and product features.It can be implemented in common applications like retail stores. Even though the algorithm needs to be trained first using supervised learning, adequate historical data is usually available with the retail store.

In this project you will be provided with historical sales data for a number of Walmart stores, for which you have to find out the department wide sales for each store. You need to possess basic knowledge of forecasting techniques to work on this project.

Data source:

Walmart Recruiting – Store Sales Forecasting


7. Reading human handwriting


This interesting project in neural networks deals with training it to recognize letter and then from there, a human’s handwriting. Neural networks are in the buzz for image recognition and self driving cars, and so make for an exciting topic for the beginner to work on.

MNSIT Handwritten Digit Classification Challenge provides you with a manageable dataset from which you can start working. This data is easy on beginners and small enough to fit in a single computer. High computational power is not required in this project.

Going through the first chapter in the tutorial mentioned below will help you in making a neural network from scratch to solve this challenge with high accuracy.

Tutorial:

Neural Networks and Deep Learning (Online Book)

Data source:

MNIST


8. Predicting influencers in the social network


In social media platforms like Instagram, individuals induce “word of mouth” effects. This leads to popularization of a product, or an activity such as singing a particular song. Identifying such people is important as it leads to a vast spread of information in the network. It also helps companies to go beyond direct marketing and look for users in the social media that can influence others to promote their product.

By looking at these insights, marketers can strategize new ways to market a product and identify key individuals across these platforms that can help them do so. The feedback generated will also help them identify the target group of people on which to promote the product.

For this project you need to take into account the influencing capabilities of an influence and their friends. The factors under use here are utilized by machine learning tools like data mining and natural language processing and sentiment analysis.

Data source :

PeerIndex