Top 10 Machine Learning Projects for Beginners

If you are an aspiring Data Scientist, working on machine learning projects is the best and most effective way to get a hands-on experience that will help you break into the sought-after field of Machine Learning and Data Science.

We will also be discussing the steps to follow pre and post creation of your machine learning models. Following are the sections we have covered:

• Top 10 Machine Learning Projects for Beginners
  • Breast Cancer Analysis
  • Amazon Recommender System
  • Twitter Sentiment Analysis
  • Stock Price Prediction
  • Life Expectancy Prediction
  • Bitcoin Price Prediction
  • Annual Salary Prediction
  • MNSIT Handwritten Image Recognition
  • Fake News Detection
  • Customer Segmentation and Market Basket Analysis
• How to start with your project?
• How to create a summary of your project?
• Tips on How to Include Machine Learning Projects in Your Resume

Top 10 Machine Learning Projects for Beginners

1. Breast Cancer Analysis

Project Type:

Unsupervised Learning Technique: Clustering

Project Description

In this project, you will be using a K-means clustering algorithm in machine learning to detect breast cancer based on target attributes.

Dataset: Breast Cancer data

Tutorial Notebook: Cluster analysis of Breast Cancer dataset

2. Amazon Recommender System

Project Type:

Recommender System using Collaborative Filtering

Project Description

In this project, you will be building a recommendation system to predict the ratings and popularity of products on the e-commerce website, Amazon. Currently, Amazon uses item-item collaborative filtering to recommend products, which you will be using for building your recommender system as well.

Dataset: Amazon Product Reviews data

Tutorial Notebook: Recommender System Using Amazon Reviews

3. Twitter Sentiment Analysis

Project Type:

Natural Language Processing (NLP)

Project Description

In this project, you will be developing a Sentiment Analysis model using NLP techniques to categorize a tweet as Positive or Negative.

Dataset: Twitter data

Tutorial Notebook: Twitter Sentiment Analysis for Beginners

4. Stock Price Prediction

Project Type:

Deep Learning: Long short-term memory (LSTM)

Project Description

In this project, you will be analyzing the risk of a stock based on its previous performance history. You will also be predicting future stock prices using LSTM.

Datasets can be found here.

Tutorial Notebook: Stock Market Analysis and Prediction

5. Life Expectancy Prediction

Project Type:

Supervised Learning Technique: Regression

Project Description

In this project, you will be using the Random Forest regressor model to predict the life expectancy of an individual based on features such as education, alcohol consumption, and adult mortality.

Dataset: Life Expectancy data

Tutorial Notebook: Life Expectancy Regression

6. Bitcoin Price Prediction

Project Type:

Time Series Analysis

Project Description

In this project, you will be predicting the price of the most popular cryptocurrency –Bitcoin. You will be working with the Time Series ARIMA model. The time series forecasting problems analyze patterns in the time-dependent data to make predictions about the future.

Dataset: Bitcoin Historical data

Tutorial Notebook: Bitcoin Price Prediction by ARIMA

7. Annual Salary Prediction

Project Type:

Supervised Learning Technique: Classification

Project Description

In this project, you will be using the Naïve Bayes algorithm in machine learning to predict whether an individual makes over $50K a year or less.

Dataset: Adult data

Tutorial Notebook: Salary Prediction Using Naïve Bayes Classifier

8. MNSIT Handwritten Image Recognition

Project Type:

Deep Learning: Convolutional Neural Networks (CNN)

Project Description

In this project, you will be using CNN to identify digits from a large dataset containing thousands of handwritten digit images. You will also be increasing the efficiency of your deep learning model by using augmentation.

Dataset: Digit Recognizer data

Tutorial Notebook: MNSIT – CNN with Augmentation

9. Fake News Detection

Project Type:

Supervised Learning Technique: Classification

Project Description

In this project, you will be performing feature extraction on the given dataset and detect whether the news is fake or not. You will be using various machine learning classification algorithms including Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest.

Dataset: Fake News Detection data

Tutorial Notebook: Fake News Detection

10. Customer Segmentation and Market Basket Analysis

Project Type:

Unsupervised Learning Technique: Clustering and Association

Project Description

In this project, you will first be performing customer segmentation to uncover customer buying patterns. Then, you will perform a market basket analysis on that data to gain insight into the purchase behaviour of customers.

Dataset: Online Retail data

Tutorial Notebook: Customer Segmentation and Market Basket Analysis

How to Start The Project?

• Identify your problem.

• Figure out your AI solution to address the problem.

• Acquire your data. Open-source datasets are readily available on platforms like Kaggle, as have been linked above.

• Prepare your data. This step involved cleaning your data to eliminate irrelevant features or values from it.

• Transform your data, particularly if it is unstructured.

• Perform Exploratory Data Analysis (EDA) to uncover hidden patterns and relationships between your data features.

Once your data is prepared and ready, you can proceed with the development of your ML model.

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How to Create a Summary of Your Project?

Document your ML project and provide relevant information required to reproduce your work. Outline the problem statement clearly, propose your machine learning solution to it, and provide evidence of your solution’s success.

Evaluate your ML project using performance metrics to measure the predictive accuracy of your model depending on the problem type. Include this in your project summary document.

Last but not the least, state the data sources.

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Tips to Include ML Projects in Your Resume

• Frame your contributions to the project while stating the key metrics in order to convey your accomplishments.

• Clearly mention the title of your ML project. It would be even better if you include a link to your project(s) on your resume.

• Describe your project briefly and emphasize the skills, tools, and programs you used throughout your project.

• Highlight any skills that overlap with the ones mentioned in the job description of your desired role.

• Demonstrate how your ML project adds to the business value in the role you are applying for.

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Endnotes

The machine learning projects for beginners will help you demonstrate experience in machine learning through supervised and unsupervised techniques. We have also included deep learning projects to help you get a better understanding of the kind of skills that are in demand in the current job market. Data Science and Machine Learning are increasingly growing domains that have hugely impacted big businesses worldwide. Interested in being a part of this frenzy? Explore related articles here.

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