50+ Data Science Project Ideas To Help You Learn By Doing

Data Science Projects for Practice (Unsplash)

If you recently completed a data science Bootcamp you likely have a couple of data science projects in your portfolio already. But what if you part-timed the Bootcamp and only have 2 -4 beginner-level projects like the IRIS Flowers Classification or Predicting the Survival on the Titanic? What if you don’t have a data science portfolio because you cannot afford or don’t want to pay for a pricey Bootcamp? Then this article is a must-read for you. You will need to get more data science skills and projects under your belt before appearing for your data science job interview. All you need is the grit to build data science skills on your own and some fantastic data science projects to show to your next hiring manager.

The learning path to becoming a data scientist is often bothersome, specifically if you’re faced with the chicken and egg problem. To get a data science job, you need real-world experience working on data science projects. But, to get that real-world experience, you need a job….right?

Today I will help you learn how to generate your own data science industry experience by practicing diverse data science projects….many of which are apparently hidden in plain sight.

Before Diving In, Establish Some Data Science Career Goals

Before we dive into where and how to start practicing and developing data science projects, let’s begin with your goals. Just like any other data science project has business goals, think of yourself as a one-person business to build a solid job-winning data science portfolio. The companies hiring for data science job roles- have the goal of finding someone who fits their business requirements and wants, which can best be evaluated through a portfolio.

Before you achieve your career goals, you first have to define them.

So let’s get a little into understanding your data science career goals :

• Are you focusing on landing a data science job in a particular industry, e.g., finance, retail, or Healthcare?
• Where do you want to work, or do you have any specific dream organizations you want to work for?
• Do you want to work at a well-established organization or a startup?

It helps to understand that different organizations look for different data science skills. Suppose you’re looking to join the retail industry. In that case, portfolio projects like Sales Forecasting, Recommender Systems, Market Basket Analysis might carry more weight on your portfolio than, say, a medical image segmentation project.

Understandably, many data enthusiasts are looking for a decent data science job. Keep those data science career goals in your head as you evaluate these data science project ideas for your portfolio.

50+ Data Science Project Ideas To Kickstart Your Career — Learn By Doing

This is a rundown of fantastic data science project ideas that will set off your career in the industry. Given, there are tons of projects that could help you learn or perfect some basic or complex data science and machine learning tasks. But if you are a data science beginner who is somewhat skeptical about venturing out on your own, the data science projects in this article have been handpicked and solved for you.

These projects cover various concepts in data science, including NLP, Computer Vision, Classification, Regression, Neural Networks, Time Series, etc., plus they are customizable to suit your requirements. These data science projects have been designed to fine-tune your data science skills and ensure that you are on course to becoming a superhero data scientist in no time.

With that said, let’s jump right into the innovative data science project ideas! Feel heroic, you will make it through with the commitment to learning. As promised, these project ideas have just simple beginner-level data science projects to complex advanced-level projects to challenge yourself.

Data Science Projects using Classification

Classification Projects on Machine Learning for Beginners

This machine learning project will assist you in developing a basic grasp of various classification algorithms if you are a newbie in the field of data science.

Classification Projects for Machine Learning

Objective: Forecast the status of a business’s license request. It predicts whether the license will be issued, canceled, revoked, revoked and appealed, or placed on hold.

Dataset: A licensed dataset is used in this project. It has information on 86K individual businesses, each with its own set of attributes. The dependent variables include the license status such as AAI (issued), AAC (canceled), REV (revoked), etc. The independent variables include the timeline of application status, business location, payment details, etc.

The license status, which is separated into the five categories listed above, is the dependent variable or target. Also, the project addresses common dataset challenges at scale such as data imbalance, missing data, data leakage, outlier treatment, etc.

Tech Stack — The project is implemented in Python and uses pandas (for data manipulation), numpy, matplotlib (for data visualization), category_encoder (for categorical variable encoding), os, etc.

Algorithms: Being a beginner-level classification project, it implements the KNN algorithm, Naive Bayes, Logistic regression, and decision tree classifier.

Build Classification Algorithms for Digital Transformation[Banking]

Use Python programming language to construct a machine learning strategy that will analyze the digitalization process of bank clients using several classification algorithms.

Objective: Effective campaigns with better target marketing raise conversion ratios to double digits while maintaining the same budget. The machine learning model will perform targeted digital marketing by forecasting which clients will transition from liability to asset status.

Dataset: There are two CSV files in the dataset-

• Data 1 consists of 5000 rows and 8 columns.
• Data 2 consists of 5000 rows and 7 columns.

Data 1 includes the basic details about the customers, such as customer ID, age, zip code, the highest amount spent by the customer, etc. Data 2 includes the banking information related to the customer, such as mortgage, customer’s fixed deposit account, customer’s security asset, whether the customer has a credit card, etc.

Tech Stack: Python is used as the programming language for this project, and imports the following libraries- numpy (for arrays and vector operations), pandas (for dataframes), seaborn (for high-end graphical capabilities), and matplotlib.pyplot (for plotting functions).

Algorithms: Various algorithms, such as Logistic Regression, Naive Bayes, Decision Tree Classifier, and others, are used in this classification project.

Credit Card Fraud Detection as a Classification Problem

Objective: Based on certain parameters, this project seeks to identify credit card transactions as genuine or fraudulent. This prediction is based on variables such as a customer’s average amount per transaction, average amount transacted per week, customer location for a transaction, and so on.

Dataset: The dataset used here is an open-source credit card fraud detection dataset from Kaggle. It includes credit card transactions done by European cardholders in September 2013. In this dataset, we have 492 frauds out of 284,807 transactions that occurred in a span of two days. The excessively high degree of imbalance in the dataset is due to the positive class (frauds) accounting for only 0.172 percent of all transactions. Oversampling and undersampling techniques are employed to handle this imbalance. A cross-validation framework employs a variety of classification techniques.

Tech Stack: We work on a Python notebook for this project, where we load data processing modules like numpy and pandas, as well as statistical data visualization modules like matplotlib and seaborn.

Algorithms: Support Vector Machine, Decision Tree, and k-Nearest Neighbour are some of the non-linear algorithms used in building this prediction model. In addition to these, logistic regression is used.

Predict Churn for a Telecom Company

This data science project predicts client churn for a telecom company is predicted, along with the key factors that trigger the churn.

Objective: The project aims to build an R-based logistic regression model to forecast the likelihood of churn for each client of a telecom company. The prediction model output serves as an early warning system, indicating that some customers are likely to churn. The primary factors that cause customers to churn can be addressed to guarantee that customers are retained.

Dataset: The prediction model for this project is based on customer data from a US-based telecom firm. We have all of the relevant customer information in the dataset, including the client’s location (state, area code, etc.), whether or not the consumer has any international or voicemail plans, and so on. The ‘churn’ variable in the dataset is our dependent variable in this scenario.

Algorithms: Machine learning algorithms such as Logistic Regression and Decision trees are implemented in this classification project.

German Credit Dataset Analysis to Classify Loan Applications

In this data science project, we will use R to classify loan applications using German credit datasets by implementing various classification algorithms such as Logistic Regression, Decision Trees, Bayesian model, and others.

Objective: The project aims at implementing various data preprocessing techniques, Logistic Regression, etc. in order to extract a set of useful attributes related to the customers and then, using those attributes to train a neural network model to classify the tuples in the given dataset in order to predict which customer will receive the credit.

Dataset: Use the German Credit Dataset with attributes such as customer information such as the status of a customer’s existing checking account, the credit history of the customer, the purpose of the loan application, address and contact details of the customer, etc to classify loans.

Tech Stack: The project leverages popular R programming libraries such as lattice, ape, knitr, gplots, caret, randomForest, etc.

Algorithms: The project implements the Naive Bayes algorithm, Logistic Regression algorithm, Decision Trees, Random Forest algorithm, etc.

1. Deep Learning with Keras in R to Predict Customer Churn
2. Build an Image Classifier for Plant Species Identification
3. TalkingData AdTracking Fraud Detection
4. Ecommerce Product Reviews — Pairwise Ranking and Sentiment Analysis
5. Loan Eligibility Prediction using Gradient Boosting Classifier
6. Customer Churn Prediction Analysis using Ensemble Techniques
7. Human Activity Recognition Using Multiclass Classification in Python
8. Loan Eligibility Prediction in Python using H2O.ai
9. Build a Multi-Touch Attribution Machine Learning Model in Python

Check Out Other Interesting Classification Project Ideas

Data Science Projects using Regression

Machine Learning Project to Forecast Rossmann Store Sales

This machine learning project will create a prediction model that will aid in predicting future sales for Rossmann stores on a daily basis using their retail data as input.

Objective: With over 3,000 stores distributed across seven European countries, the accuracy of Rossmann’s upcoming six-week daily sales forecast could be challenged because store managers calculate them based on their individual circumstances. Therefore, we build a prediction model to help them make accurate daily sales forecasts for their 1,115 store locations spanning across Germany. These forecasts will help store managers boost staff productivity, enhance customer experience, and drive revenue.

Dataset: The prediction model is developed with help of the dataset formed by merging the store dataset and the training dataset that we have. The attributes in the store dataset include store details such as store id, store type, assortment type, number of customers per day, sales on any given day, competitor store distance, etc. The training dataset includes attributes such as store id, the date on which sales were made, the number of sales made on that date, the number of customers on that date, etc.

Tech Stack: This project uses the Python programming language, and some of the libraries used are pandas, numpy, seaborn, matplotlib.pyplot, etc.

1. Machine Learning project for Retail Price Optimization
2. BigMart Sales Prediction
3. Wine Quality Prediction in R
4. Predictive Models in IoT — Energy Prediction Use Case
5. Inventory Demand Forecasting using Machine Learning in R
6. All-State Insurance Claims Severity Prediction
7. Predict Macro-Economic Trends using Kaggle Financial Dataset
8. House Price Prediction Project using Machine Learning

Check Out Other Interesting Regression Project Ideas

NLP Projects

1. Natural Language Processing Chatbot application using NLTK for Text Classification
2. Resume Parsing with Machine learning — NLP with Python OCR and Spacy
3. NLP Project on LDA Topic Modelling Python using RACE Dataset
4. Abstractive Text Summarization using Transformers-BART Model
5. Create Your First Chatbot with RASA NLU Model and Python
6. Word2Vec and FastText Word Embedding with Gensim in Python

Check Out Other Interesting NLP Projects Ideas

Computer Vision Projects

1. OpenCV Project for Beginners to Learn Computer Vision Basics
2. OpenCV Project to Master Advanced Computer Vision Concepts
3. Digit Recognition using CNN for MNIST Dataset in Python
4. Build a Similar Images Finder with Python, Keras, and Tensorflow
5. Medical Image Segmentation Deep Learning Project
6. Image Segmentation using Mask R-CNN with Tensorflow
7. Build OCR from Scratch Python using YOLO and Tesseract
8. Forecasting Business KPI’s with Tensorflow and Python
9. Build a Face Recognition System in Python using FaceNet
10. Real-Time Fruit Detection using YOLOv4

Check Out Other Interesting Computer Vision Projects

Time Series Projects

1. Walmart Sales Forecasting Data Science Project
2. Ola Bike Rides Request Demand Forecast
3. Avocado Price PredictionTime Series Project in Python
4. Time Series Python Project using Greykite and Neural Prophet
5. Demand Prediction of Driver Availability using Multistep Time Series Analysis
6. Time Series Analysis Project in R on Stock Market forecasting
7. Time Series Project to Build an Autoregressive Model in Python

Recommended Reading — Time Series Forecasting Projects Ideas

Deep Learning and Neural Network Projects

1. Time Series Forecasting with LSTM Neural Network Python
2. NLP and Deep Learning For Fake News Classification in Python
3. Credit Card Anomaly Detection using Autoencoders
4. Multi-Class Text Classification with Deep Learning using BERT
5. Build CNN for Image Colorization using Deep Transfer Learning
6. Deep Learning Project for Text Detection in Images using Python
7. Text Classification with Transformers-RoBERTa and XLNet Model

Check Out Other Deep Learning Project Ideas and Neural Network Project Ideas

MLOps Projects

1. MLOps Project for a Mask R-CNN on GCP using uWSGI Flask
2. FEAST Feature Store Example for Scaling Machine Learning

MLOps Project Ideas for Practice

What Are Some Good Data Science Projects for Resume?

If you’re a data science beginner and don’t know what kind of data science projects you should add to your resume when applying for a data job at any of the top tech companies, this list of data science projects is for you to practice and add them to your resume. Machine learning engineers or data scientists already working in the industry should ensure that their organization implements these data science projects to boost their business ROIs.

1. Data Science Project to Build a Recommender System

Do you know Netflix’s Recommendation engine is worth ​​$1 billion annually? Yes, you read that right. Netflix estimates that it would lose $1 billion every year if it did not have a personalized recommendation engine.

Whether it is recommending jobs, products, videos, or music, organizations across the world use recommender systems to drive delightsome user experience while driving incremental revenue. Recommendation systems are built to recommend products to users based on the historic data provided by the user and other attributes that take care of the user’s preferences. They find a match between the user and a product to predict the products that the user is most likely to purchase. It also helps the users get personalized recommendations of products relevant to them, saving money and time. This is highly beneficial to both the users and organizations.

Recommender systems are classified into two categories: Content-Based and Collaborative Filtering systems.

Content-Based Recommender System

A content-based recommender model will use additional information about the customer and items to make predictions. For example, if a customer searches for a video game, a content-based system may consider other parameters like occupation, gender, age, and other personal information of the user for making recommendations.

Collaborative Filtering Recommender System

Collaborative filtering recommenders use similarities between products and users to provide personalized recommendations. It is based on the notion that people like products similar to the other products they like, and also products that are liked by people having similar tastes and preferences.

To build a collaborative filtering model, you can start with loading your dataset into a pandas data frame. From the surprise module import the KNNWithMeans model. Surprise is a python module used for building and analyzing recommendation systems, using rating data. Build a user rating matrix using this code:

KNNWithMeans(k=5, sim_options={‘name’: ‘pearson_baseline’, ‘user_based’: False})

You can use the user_based parameter (true/false) to switch between user-based or item-based collaborative filtering. Here k=5 refers to the number of neighbors considered for aggregation. Use the training dataset to train the model and validate it using the test dataset.

You can use the Amazon Product Reviews Dataset available on Kaggle for this project.

2. Data Science Project for Sentiment Analysis of Customer Reviews

Sentiment Analysis is the process of determining whether a given input of text is positive, negative, or neutral. A sentiment analysis model combines NLP ( natural language processing) and ML ( machine learning) techniques to assign a score to various entities, themes, topics, and categories in a sentence. This can help businesses understand the customer experiences from feedbacks and reviews, gauge public opinion, and conduct market research. You can build a simple sentiment analysis model using a simple multilayer perception.

· Download the Women’s Ecommerce Clothing Dataset to start implementing this project.

· Visualize the dataset using seaborn and matplotlib libraries. These can help you can valuable insights about your data. Plotting histograms and heatmaps can help with the data analysis.

· Use the TF-IDF vectorizer on the review text and create an encoding. Also, use frequency of words as an additional parameter.

· Use a multilayer perceptron to classify the data into positive, neutral, or negative.

· Alternatively, you can build your sequential model using Keras and use either ‘relu’ or ‘sigmoid’ as the activation function. Use this model to build your classifier using the training dataset. Remember to cross-validate using the testing data.

3) Warranty Claims Prediction Project
Warranty is a service provider /product manufacturer’s commitment and guarantee of quality as promised to the customer. The warranty assures the customer that if any problem arises with the product within the warranty period, it will be replaced without any monetary burden. Warranty analytics can help ascertain the quality and reliability of the products. It also allows businesses to identify faulty products and fix them before they cause any significant losses. You can build machine learning models to detect anomalies in warranty claims, identify claims patterns and products flaws.

· Download the Warranty Claims dataset.

· Load your training dataset into a pandas data frame and clean the data. Make sure you don’t have any missing or NaN values. Remove duplicate columns, if any.

· You can start with a decision tree classifier from the sklearn library.

· Train the model and cross-validate it using the test dataset.

· Use the classification report metrics for evaluation. You can also try other ML algorithms like Support vector classifiers (SVC), linear regression, logistic regression and compare the results.

What To Do Next?

This is a wrap-up of our solved end-to-end data science project ideas aimed at helping any beginner make a successful career transition into data science and analytics. Generating your own data science experience is not an easy feat, but it’s not impossible either. With some hard work and commitment, you can leverage these data science project ideas to build a fantastic portfolio that will get you hired.

Data science enthusiasts need to show flexibility to land the best rewarding data science jobs. By building a varied portfolio of data science projects, you can give hiring managers the impression that you’re that person. Now it’s over to you. Just put in the time and dedication, show your data science skills on the portfolio, and you’ll be landing your dream data science job in no time.

Hungry for More Data Science Project Ideas with Source Code? Check Out other best data science and machine learning projects for practice with source code.