Top 10 Machine Learning Project Ideas For Beginners In 2020
Due to Covid19, lots are forced to stay at home. This also brings the opportunity to add machine learning knowledge into your arsenal. But wait if you are not sure about how to start? Keep reading this post, you will get the answers by the end of this post.
The beginner has lots of unanswered questions and often confused about where to start. So, if you have learned the basics of python, and machine learning then it is a good idea to open your python IDE and starts practicing.
The machine learning projects list prepared while keeping students or beginners in mind. While you are learning it is recommended not to fascinate with a large or complex machine learning project. Your primary goal should be to build the concept first and then practice enough to embed these concepts. Without further ado, let us get started with the Machine Learning project ideas.
Machine Learning Projects
1. Boston Housing Price
Linear regression is the first most algorithm that any data scientists learn. So, the regression dataset is naturally a good idea to make your hands dirty. Boston house prices is a small dataset with 506 observations and contain information about houses in Boston. You need to build a regression model to predict the selling price on the house based on input features. Boston Housing dataset can be downloaded from the UCI Machine Learning Repository. Boston Housing consists of the following data points:
- CRIM – per capita crime rate by town
- INDUS – the proportion of non-retail business acres per town
- CHAS – Charles River dummy variable (1 if the tract bounds river; 0 otherwise)
- NOX – nitric oxides concentration (parts per 10 million)
- RM – the average number of rooms per dwelling
- AGE – the proportion of owner-occupied units built prior to 1940
- DIS – weighted distances to five Boston employment centres
- RAD – index of accessibility to radial highways
- TAX – full-value property-tax rate per $10,000
- PTRATIO – pupil-teacher ratio by town
- B – 1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT – % lower status of the population
- MEDV – Median value of owner-occupied homes in $1000’s
- ZN – the proportion of residential land zoned for lots over 25,000 sq.ft.
2. Titanic: Machine Learning From Diaster
One of the legendary Titanic classification problems. I guess it is rare to find data scientists who did not work on this dataset. I think you are glad to know that most of the real-world machine learning projects are classification. You can download the dataset from Kaggle. Strongly recommends these two awesome blogs:
3. Wine Quality Prediction
Another good dataset to do hands-on and give you a chance to build a classification model. You need to build a machine learning model to predict the quality of wines by exploring their various chemical properties. Wine quality dataset is a small dataset and allows you to explore explanatory data analysis. Also, you can build regression as well classification model on this dataset. Wine quality dataset can be downloaded from the UCI Machine Learning Repository.
4. Iris Flower Multi-class Classification
By now you have learned binomial classification, so what about the problem that cannot be answered as Yes or No. IRIS flower allows you to build a multi-class classification model to classify among three species (Setosa, Versicolor, or Virginia) from measurements of sepals and petals’ length and width. You can also build an unsupervised machine learning algorithm. Iris dataset can be downloaded from the UCI ML Repository. Answer this question in the comment section: Can you apply logistic regression to IRIS flower?
End-to-end web application built on Python, Django, Machine Learning libraries. Machine Learning algorithms deployed inside application are Decision tree, Support vector machine or SVM, Naive Bayes, and K-Nearest neighbors.
5. Stock Price Prediction
Enough of Regression and Classification problem is there anything else to learn. There is a lot, the machine learning field is such a vast field. Stock price prediction may make you billionaire if you get it right (Try it at your own risk 😊). So how it differs from another machine learning dataset that we discussed until now? The stock price is a time-series dataset and you apply your time series knowledge here. You can also convert and solve stock price prediction using regression and classification.
Web application built on Python, Django and uses deep learning framework Long Short Term Memory or LSTM. Click here to read more about it.
6. Sales Forecasting
Time series is a complex topic to learn, so probably one example is not enough to gain confidence. So here is another time series prediction problem. Walmart’s sales forecasting dataset can be downloaded from Kaggle. Walmart shared historical sales data for 45 Walmart stores located in different regions. Dataset provided with features such as store number, department number, date, sales, and special holiday week. The goal of this machine learning problem is to predict the sales for each store.
7. Loan Prediction
Hopefully, you have followed the blog post all along. Now be ready to work on a slightly complex classification problem. This data is related to the direct marketing campaigns of a Portuguese banking institution. The goal of the machine learning algorithm is to predict if the client will subscribe to a term deposit. Pay close attention to understand the relationship between input features and target variables, also leverage the power of feature engineering to build a robust and accurate model.
8. Absenteeism At Work
Have you ever imagined that your company may be tracking your leaves and applying machine learning to find similar groups based on the employee’s attributes. You can download this interesting dataset from UCI Machine Learning. You can apply regression machine learning algorithms. As an additional exercise, incorporate the features from the clustering model and see if this increases the accuracy of your regression model.
9. Credit Card Fraud Detection
Most of the real world’s problem is imbalanced. If you did not practice well on an imbalanced problem, then it can be a serious interview spoiler. You can expect a good portion of your interview focused on an imbalanced dataset. Credit card dataset is highly unbalanced, the frauds comprise of 0.172% of all transactions (492 frauds out of 284,807 transactions). Credit card fraud dataset can be downloaded from Kaggle. Check the accuracy of your model and tell me in the comment section what you have learned from this dataset?
10. House Prices: Advanced Regression Techniques
You might be thinking of why this problem is recommended again. Two reasons: 1. dataset is complex and has a good mix of categorical and numeric variables 2. Challenges you to extend your learning to deal with such many features. You need to be innovative enough to perform explanatory data analysis. Housing prices dataset can be downloaded from Kaggle.
Machine learning applies to a wide variety of problems, and it is natural to get confused about what to learn or what to not. I have tried my best to put my experience in words to ease your journey to become a data scientist. Always remember the goal of machine learning is to solve the problem and not limited to create models. So as a genuine piece of advice, do not chase several models to learn rather focus on the concepts.
In the live project, model building effort is just 5-10% of total project effort and 60-70% time spent on data understanding and processing. So, if you are doing otherwise then rethink your approach. Make sure you follow the right path toward your journey to becoming one.