Beginner’s Guide to Easy Projects in Data Science and Machine Learning
Let’s take a look at the projects that will help you build a successful career as a data scientist and in machine learning.
About Data Science And Machine Learning
Data science combines machine learning and human insights. As businesses have huge amounts of data, they are actively looking for data scientists to enhance their decision-making and to run a successful business.
Data science now plays a key role in areas like analytics, data mining, natural language processing (NLP), machine learning (ML), and AI. Due to the demand for data scientists in the market, the salaries expected are also high.
The average salary for a data scientist in the United States is around $153,000, while in India it is around 13 LPA. These high-demand jobs also require specific skills.
The best way to showcase data science skills is with a strong portfolio of projects. These projects help you stand out and secure a position in the field. Such projects can also help improve programming skills and give job seekers a competitive edge.
The following projects cover a wide range of topics, including classification, regression, and deep learning. The project ideas are divided into two categories: BI projects, which benefit both data analysts and data scientists, and machine learning projects, aimed specifically at data scientists.
BI And Data Analysis Projects:
Here is a list of data science and business intelligence (BI) projects:
Sales Insights Dashboarding
Generates sales insights for a business. SQL and dashboard tools are used in business intelligence applications such as Power BI and Tableau. Skills include data cleaning and ETL (Extract, Transform, Load). Customisation is carried out using datasets from Kaggle or Nasdaq Data Link.
Start with a problem statement. Plan the project and investigate the data. Perform simple analysis using SQL. Pull data into customisation. Power BI or Tableau. Gather customer feedback to create a new version.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Sales Insights Dashboarding | SQL, BI tools, such as Power BI and Tableau. Data cleaning and ETL (Extract, Transform, and Load). Customise datasets from Kaggle or Nasdaq Data Link. | 1. Start with a problem statement, followed by project planning and data discovery. 2. Perform simple analysis using SQL. Pull data into Power BI or Tableau. 3. Conduct data cleaning and ETL and build dashboards. 4. Take feedback from customers and build a version | Generating sales insights for a business. It is a very practical corporate-style project. Testimonials show people got a job or internship based on this project. |
Financial Management Dashboard
Personal finance dashboards help you get answers about your finances and expenses. Creating different dashboards can be helpful. They can be tailored to small and large businesses, allowing us to analyze relevant data.
We use visualization tools like Power BI to create dashboards, and we can keep track of expenses in Excel. First, create an Excel file with your personal expenses. Then, create a personal finance dashboard in Power BI and use the data to generate various types of charts.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Personal Finance Dashboard | Power BI. Use an Excel file for expenses. | Create an Excel file containing your own expenses.Build a personal finance dashboard in Power BI.Use the data to draw various kinds of charts. | Getting answers on your personal finances/own expenses. Building various dashboards that can be quite useful. |
Machine Learning (ML) and Deep Learning Projects
These projects are primarily for a data scientist position and cover diverse topics like classification, regression, deep learning (including NLP and computer vision), and clustering.
Sport Celebrity Image Classification (Classification)
This project involves building a complete website that can classify a specified celebrity image. It includes a wide range of machine learning topics, specifically classification.
The process has several steps: first, image data is collected using a Chrome extension. Next, data cleaning and preprocessing techniques are applied. Then, a model is built; in the example provided, a Support Vector Machine (SVM) is used, although Convolutional Neural Networks (CNN) are usually preferred for image classification.
Grid Search CV is used to optimise hyperparameters. Finally, the model is deployed by running it inside a Flask server, which is a Python web server. The user interface comprises simple HTML, CSS, and JavaScript. This basic idea can be tailored to classify movies. stars, spiritual leaders, political figures, entrepreneurs, or even pictures of family members.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Sport Celebrity Image Classification | Image data collection (using a Chrome extension).Data cleaning and preprocessing techniques.*Model building using Support Vector Machine (SVM)*Optimisation using Grid Search CV.Deployment using a Flask server (Python web server).*The UI uses simple HTML, CSS, and JavaScript. | Perform image data collection. Apply data cleaning and preprocessing techniques. Build a model (e.g., SVM). Perform optimisation using Grid Search CV for hyperparameter tuning. Deploy the model, running it inside a Flask server. Build the UI using HTML, CSS, and JavaScript. | Building an end-to-end website that can classify the specified celebrity image. Building the entire application allows for learning a lot of useful skills |
Bangalore Property Price Prediction (Regression)
This is a regression project. The goal is to create a complete website where users can input details like the number of bedrooms or square footage, and the site will predict the home’s price.
While predicting home prices is a common challenge, it can be made unique by applying it to a different city, such as New York or Bangkok, or by tackling a different regression problem entirely, like forecasting scores in cricket, for example, how much Virat Kohli will score in the next match.
The model was trained using simple linear regression after steps such as outlier removal, dimension reduction, and feature engineering. Hyperparameter tuning was done using Grid Search CV, and the model was deployed on the Amazon AWS cloud with a Flask server handling requests from the website.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Bangalore Property Price Prediction | Simple linear regressionhyperparameter tuning with Grid Search CV deployment with Amazon AWS a Flask server. Techniques feature engineering. | Perform outlier removal. Conduct dimensionality reductionUse feature engineering techniques. Train the model using simple linear regression. Optimise the model using Grid Search CV. Deploy the model to the Amazon AWS cloud. Run a Flask server to serve requests coming from the website. | Building an end-to-end website where a user can enter parameters (e.g., bedrooms, square footage) and receive an estimated price for that home. Building the complete solution allows you to learn a variety of technical skills. |
Plant Disease Classification (Deep Learning / Image Classification)
This project involves building a mobile application developed in React Native that uses deep learning to detect diseases in plants. The app takes a picture of a plant and quickly identifies the type of disease it has.
The model in the source’s example was originally designed to classify potato diseases. The concept can be tailored for classifying diseases in tomatoes.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Plant Disease Classification | Deep learning. Mobile application development in React Native. Data cleaning and Preprocessing with tf dataset and data augmentation. Model building using Convolutional Neural Network (CNN). Exporting the model into a TensorFlow Lite model and TensorFlow Serving. Deployment using a Google Cloud function. | Clean and preprocess data using the tf dataset, as well as data augmentation. Build the model using Convolutional Neural Network (CNN). Export the model as a TensorFlow Lite model for TF Serving. Build a website first, and then build the mobile application. Export the TensorFlow Lite model as a Google Cloud function. Build the React Native app that makes calls to Google Cloud Functions. | A mobile application that can take a picture of a plant and immediately determine what disease it has. Learning useful cloud tips |
Loan Approval System using Machine Learning (Classification)
This machine learning project aims to analyse and predict the outcomes of loan approval processes for individuals. It approaches the issue as a classification problem. Classification problems include predicting class labels for input data examples, much like identifying spam emails or cancer.
The steps involved in this system are data collection, preprocessing, feature selection, model training, model testing, and prediction.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Loan Approval System using Machine Learning | Machine Learning (ML). | Data collection, pre-processingfeature selection, model trainingmodel testing, and prediction. | Analyzing and predicting the outcome of loan approval processes for individuals |
Personality Prediction System
This system uses machine learning techniques to determine an individual’s personality, which is relevant as companies often select candidates based on personality traits.
The approach aims to combine ML techniques like SVD (singular value decomposition) and logistic regression to predict personality. In addition, the project uses the phrase frequency method to forecast an individual’s abilities.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Personality Prediction System | Machine learning techniques. SVD (Singular Value Decomposition) and logistic regression. Phrase frequency method. | Aim to combine ML techniques like SVD and logistic regression to predict personality. Use the term ‘the frequency method’ to describe forecasting abilities. | Determining an individual’s personality. Enables users to quickly identify their personality traits and technical abilities |
Time Series Forecasting
Time series forecasting predicts future values by looking at patterns in historical data. This knowledge is essential for every data scientist because it applies to many areas, such as weather prediction, sales projection, and stock price forecasts. The technique usually uses a two-part approach.
The autoregressive component uses weighted previous values, while the moving average component applies weights to earlier estimated errors in the time series.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Time Series Forecasting | Methods for time series execution. Autoregressive component. Moving average component. | Employ a dual strategy: the autoregressive component leverages weighted previous values Transfer the average component’s assigned weights to the previously estimated time series errors. | Predicting future values by analyzing patterns in historical data |
Parkinson’s Disease Prediction
This machine learning project aims to create a model to check for the presence of Parkinson’s disease, a progressive disorder of the central nervous system that affects movement. The project develops a Support Vector Machine (SVM) model using Python modules such as scikit-learn.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Parkinson’s Disease Prediction | Support Vector Machine (SVM) model. Python modules: scikit-learn, NumPy, and pandas. | Import the data. Extract the characteristics and labels. Scale the features. Split the dataset. Design the SVM model. Evaluate its accuracy. | Developing the model to assess the presence of Parkinson’s disease in an individual |
Speech Recognition System
This system, also known as speech-to-text, is a machine’s ability to recognise and translate spoken words into readable text. ML speech recognition relies on algorithms that model speech based on both language and sound to extract essential elements like words and sentences.
Acoustic modelling is used to detect phonemes and phonetics in speech. For this project, the pyttsx3 library is used; it is a Python text-to-speech conversion library that works offline and is compatible with Python 2 and 3.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Speech Recognition System | ML speech recognition algorithms. Acoustic modelling. pyttsx3 (Python text-to-speech conversion library). | Utilise ML algorithms that model speech based on language and sound. Use acoustic modelling. Utilise the pyttsx3 library. | Recognising and transforming words spoken aloud into readable text (speech-to-text). |
Sentiment Analysis (Opinion Mining)
Sentiment analysis, or opinion mining, is the task of ascertaining the author’s emotion or attitude expressed in a given text to decipher the underlying intention of the user.
The project utilizes a range of natural language processing (NLP) and text analysis techniques to detect, extract, and quantify potential personal information from the text, enabling classification and data manipulation. This project is based on the Amazon customer review dataset.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Sentiment Analysis (Opinion Mining) | Natural Language Processing (NLP). Text analysis techniques. Use the Amazon customer review dataset. | Utilise a range of NLP and text analysis techniques. Detect, extract, and quantify potential personal information from the text. | Asserting the author’s emotion or attitude expressed in a given text. Deciphering the underlying intention of the user. Enables classification and data manipulation |
Fake News Detection
This machine learning project focuses on discerning between fake and true news, helping the user gain the ability to differentiate accurate from fabricated information. The project employs a TF-IDF vectorizer from the scikit-learn library on the dataset.
The model is then pre-trained with a predefined passive-aggressive classifier. The model’s performance is indicated by its accuracy and confusion matrix.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Fake News Detection | Machine learning. TF-IDF vectorizer (from the scikit-learn library). Passive-aggressive classifier. | Employ a TF-IDF vectoriser on the dataset. Pre-train the model using a pre-initialised passive-aggressive classifier. The accuracy and confusion matrix serve as indicators of performance. | Discerning between fake and true news. Gaining the ability to differentiate between accurate and fabricated information |
Image Classification using CNN
This project involves developing an image processing solution using a Conventional Neural Network (CNN), which is the preferred deep neural network for computer vision problems. CNNs are vital in the rapidly growing field of deep learning.
The goal is to learn about the capabilities and widespread popularity of CNNs. This project specifically uses the CIFAR-10 data set for image classification tasks.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Image Classification using CNN | Conventional Neural Network (CNN). Utilises the CIFAR-10 data set. | Employ CNN to develop an image processing project. Dive into each stage of creating the conviction model. Utilise the CIFAR-10 data set. | Developing an image processing solution. Gaining insight into the capabilities and widespread popularity of CNNs. |
Face Recognition System
Face recognition is noted as the fastest and least intrusive form of biometric verification. This project uses the OpenCV face recognition libraries to create a face detection system.
OpenCV provides a powerful set of tools, libraries, and hardware for building real-time computer vision applications.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Face Recognition System | OpenCV face recognition libraries. | Use the OpenCV face recognition libraries to develop the system. | Developing a face detection system. Provides the least intrusive and fastest form of biometric verification |
Movie Recommendation System
This system provides recommendations for movies and TV shows based on a viewer’s history. These recommendations are useful projects for beginners.
Programmers can practice using the Python or R language, typically using data from the MovieLens dataset, which includes information generated by over 6,000 users.
| TITLE | TOOLS OR SKILLS REQUIRED | STEPS TO FOLLOW | RESULT OF THE PROJECT |
| Movie Recommendation System | Machine learning. Practice coding in the Python or R language. Uses data from the MovieLens dataset. | Aspiring programmers can practise coding with Python or R. Use the MovieLens dataset. | Providing recommendations for movies and TV shows based on a viewer’s viewing history and preferences |
The projects listed are like working prototypes of the machine. They demonstrate that you didn’t simply study the theory. You successfully built, troubleshooted, and deployed the machine in the real world. Having unique, end-to-end projects demonstrates your practical engineering skills, making your application stand out from those who only claim to know the tools.
Data Science Course with Generative AI & Python in Hyderabad
Final Thoughts
Building a strong portfolio of projects is the best way to demonstrate one’s data science skills and ability to apply those skills, which is essential for landing a job in the field. Projects like these serve as a competitive edge. If you are looking for a data science course in Hyderabad, Whitescholars is the right choice for you.
They believe in practical knowledge and also encourage students to build real-time projects. White Scholars offers data science courses in Hyderabad that will teach you the fundamentals of the field. They also offer placement services, mentorship, and certifications.
Frequently Asked Questions:
1. What are some easy projects for beginners in data science?
A. Beginners can start with projects like creating sales dashboards and tracking personal finances, as mentioned above. These projects use basic tools like Excel, Power BI, or Python libraries. At White Scholars Data Science Coaching in Hyderabad, students receive step-by-step guidance so they can practice with real datasets.
2. Why should I do beginner projects before advanced ones?
A. Beginner projects make it easy to understand the process of data collection, cleaning, and visualisation. They also establish a strong base in Python, SQL, and BI tools. White Scholars focuses on beginner projects to help learners avoid confusion and gradually progress to advanced projects with proper guidance.
3. Do I need coding skills for beginner projects?
A. Some projects, like dashboards in Power BI or Tableau, need little coding. However, knowing the basics of Python will make projects like regression or classification easier. At White Scholars Hyderabad, trainers balance coding and no-code tools so beginners can move forward smoothly.
4. How do beginner projects help in placements?
A. Recruiters want practical skills, not just theory. A simple project like predicting house prices or building a dashboard demonstrates that you can apply concepts to real-world problems. White Scholars includes these projects in their coaching, helping students feature them in resumes and during interviews.
5. Can I complete these projects without expensive software?
A. Yes. Most beginner projects use free tools like Python, Jupyter Notebook, and datasets from Kaggle. Even Power BI offers a free desktop version. White Scholars gives access to resources and guides. Students are encouraged to use tools, making learning more accessible and practical.