Programs
Updated on November 11, 2024
Data analytics is critical for every business, providing invaluable insight into steering decisions, increasing competence, and bettering clientele experience. As a beginner or a professional, participation in data analytics projects will help in skill-building by mastering material from elementary data manipulation to cutting-edge machine learning. Nowadays, organizations find workers with proficiency in data collection and cleaning, data manipulation, probability and statistics, predictive analytics, and reporting, and they favor hiring students with a variety of project experience.
In this article, we will cover the top 20 data analytics projects from the beginner level to an expert who wishes to take more calculated steps in enhancing skills with a solid portfolio of work. It covers various topics and areas, from sales predictions and sentiment tracking to fraud identification and recommendation systems. The link for the sample source code for each project is also given.
Data analytics projects often revolve around processing and interpreting data for trends, patterns or even useful information. Such projects might compromise cleaning data, visualizing data patterns, building predictive models, and statistical analysis. Thereby giving practical exposure and a learning experience to students in the field of data analytics.
Such data analytics project steps help you move one step closer to mastering the more complex skills required. Before you realize it, you will be looking at the complete data science pipeline involving everything from collecting and cleaning data to modeling it and deploying it.
Building a data analytics project offers several advantages for beginners and experts alike. Here are some compelling reasons to choose data analytics projects:
Take into account these crucial factors before beginning any data analytics projects:
For beginners, it is always recommended to learn the basics, and then move forward on building the smaller projects that focus mainly on the fundamental concepts. The beginner-level projects include customer churn prediction, sentiment analysis of tweets, loan prediction, etc. Here is the list of beginner-friendly data analytics projects:
The customer churn prediction model helps the business in knowing and minimizing the losses of the customers. We can go a step further and create a predictive model that indicates which loyal customers may stop doing business with the company in the future by gathering and evaluating clients’ information. It always consists of data cleaning, balancing the scale of datasets, and applying an algorithm for classification, such as logistic regression or decision tree.
Learning Outcomes
This project helps to develop skills in data visualization and analysis and gives first-hand experience in designing a predictive model based on supervised machine learning. You will also appreciate the performance measures that are critical in asymmetrical datasets, namely, accuracy, precision, and recall, among others.
Project Idea
Using a dataset from a telecom or a subscription service, you’ll use variables such as the type of contract, amount of monthly cost, and the interactions a customer has with the support department to be able to predict customer churn. The project consists of using data cleansing methods, feature engineering methods, and model empathy methods which aim to enhance the accuracy of the predictions.
What It Takes to Build
Real-World Applications
The customer churn models are especially necessary for companies that deal in subscription-based products such as telecommunication, SaaS, and streaming services. These models increase customer satisfaction & customer retention since the providers can reach out proactively to the customers in the high churn risk segment.
Source Code- https://github.com/codebrain001/customer-churn-prediction
Gaining sentiment through the analysis of tweets is beneficial as it assists in determining the public view of issues, companies, or events by ascribing the text into three categories- positive, neutral, or negative. This project employs highly developed natural language processing (NLP) techniques for text cleaning, as well as pre-processing, tokenizing, and subsequently classifying the text using supervised machine learning algorithms such as Naive Bayes or deep learning architectures. Allowing sentiment analysis of that nature in real time would be useful for businesses and researchers to measure public opinion and react accordingly.
Also Read: Deep Learning vs. Machine Learning: Understanding the Key Differences
Learning Outcomes
Cohesively, this project first introduces the learner to NLP workflows such as text pre-processing, which involves cleaning the text, tokenization, removal of stop-words, and implementation of some classification algorithms. Equally, it is important to appreciate that the student will be taught the application of text analysis processes over extensive unstructured data and will thus be better suited to carry out sentiment analysis.
Project Idea
You can use the Twitter API to search for tweets from the general public that include a particular hashtag or keywords, come up with clean versions of the text data, and use it to conduct sentiment analytic classification.
What It Takes to Build
Real-World Applications
Sentiment analysis reliably and widely gets applications in social media and brand monitoring through likes, comments, and feedback from customers or followers.
Source Code- https://github.com/marcossantosportos/Twitter_Sentiment_Analysis
In the retail and manufacturing sector, sales forecasting holds an integral position in planning and decision-making. In this project, the objective of the firm will serve as a guide, predicting future sales by developing time-based data trends and forecasts, managing inventory selection, and driving target objectives. Moving averages, exponential smoothing, and ARIMA forecasting parameters are some of the tools available for the analysis of sales data.
Learning Outcomes
You will be introduced to the basics of time series in its various features, which include trend, seasonality, and noise. Apart from this, you will also acquire or understand different forecasting models and how their degree of accuracy can be assessed, like RMSE (Root Mean Square Error).
Project Idea
This is about forecasting daily or even monthly sales for a retail business based on previously recorded data. You will also build a forecasting model to keep tracking seasonal effects indifferently.
What It Takes to Build
Real-World Applications
Source Code- https://github.com/the-javapocalypse/Twitter-Sentiment-Analysis
Understanding how much real estate should ideally be priced is pivotal for property buyers, sellers, and real estate firms to keep an eye on the market and make timely decisions. The project will carry out an analysis of features such as location, area, and facilities to arrive at an estimate of housing prices. Linear regression and decision trees are often used for linear regression models.
Learning Outcomes
In this project, you will work with regression models, feature engineering, and using R-squared and Mean absolute error (MAE) as metrics for validation for the models developed.
Project Idea
You will create a regression model that forecasts property values based on attributes like location, square footage, number of bedrooms, and accessibility to amenities using historical real estate data.
What It Takes to Build
Real-World Applications
Real estate predictive models help determine pricing strategies in a competitive market, analyzing investments, and valuing properties. These models can be used by buyers, appraisers, and real estate agents to make data-driven choices.
Source Code- https://github.com/shanuhalli/Project-Real-Estate-Price-Prediction
The market basket analysis is useful to determine which products have been purchased at the same time. This project utilizes association rule mining on transactional data to uncover usable patterns, such as those useful for cross-selling items or recommending them.
Learning Outcomes
You will be introduced to association rule mining concepts such as support, confidence, and lift metrics. This project also improves your data manipulation and analysis skills because transactional data is never simple to handle.
Project Idea
By taking data on transactional purchases made in a retail business, market basket analysis will be used to group items that go together. This aids in developing knowledge on how to interpret consumer buying patterns and hence improving targeted marketing strategies.
What It Takes to Build
Real-World Applications
Source Code- https://github.com/ashishpatel26/Market-Basket-Analysis
The prediction of whether a loan will be approved relies on borrower risk assessment by financial institutions as well as on simplification of the loan granting process. In this project, loan eligibility is determined or predicted from customer demographics, financials, and credit history data. Such data can be, in turn, classified using various classification models such as decision trees or logistic regression, thus enabling data-based decision-making in banks and credit institutions.
Learning Outcomes
This project includes model assessment and preparing reporting standards such as precision, recall, and F1 score used to show how accurate the prediction is.
Project Idea
Create a model that forecasts loan eligibility based on a dataset including client variables (such as income, credit score, and outstanding loans). This helps banks make decisions more quickly by categorizing applicants as eligible or ineligible.
What It Takes to Build
Real-World Applications
In banking and finance, loan eligibility models play a critical role in risk management by granting loans to borrowers who fit the requirements and lowering default rates.
Source Code- https://github.com/mridulrb/Predict-loan-eligibility-using-IBM-Watson-Studio
Fraud use of credit cards is one of the major, and perhaps the most prevalent, forms of fraud that results in financial losses. In this project, relevant historical transaction data is collected and algorithms such as logistic regression, decision trees, or even neural networks are applied to identify whether the transaction was legitimate or fraudulent. Since the model is based on supervised learning, a labeled data set is used, with emphasis placed on the issue of class imbalance since the fraudulent transactions will always be rather few.
Learning Outcomes
You will handle imbalanced datasets and evaluation metrics such as Precision-Recall, AUC-ROC, and single-out classification models, which will prove useful in detecting fraud.
Project Idea
Based on transaction data such as transaction amount, the location where it took place and frequency, create models that will assist in the detection of fraud in real-time. Techniques such as oversampling, undersampling, or SMOTE can also be tried out to address the problem of imbalanced datasets.
What It Takes to Build
Real-World Applications
To reduce fraud, this concept is essential in banking and finance. It is used by banks and payment gateways to automatically flag transactions that seem suspicious so they can take prompt preventive action.
Source Code- https://github.com/stochasticats/credit-card-fraud-detection
Predicting employee attrition allows organizations to spot potential leavers and thus implement measures to retain them proactively. HR professionals can create a specific model by using classification methods and target strategies for retention management.
Learning Outcomes
Get used to practicing classification methods, feature engineering, and model assessment. This project involves HR analytics in which every employee feature’s impact on turnover is explored such as How much do promotions figure out in job satisfaction?, and various other questions.
Project Idea
Create a model that uses HR statistics to identify employees who are at risk based on variables like role changes, recent promotions, and job satisfaction. In order to evaluate the factors influencing attrition, entails data preprocessing, model training, and interpretability.
What It Takes to Build
Real-World Applications
Attrition prediction helps HR departments and organizations retain employees by identifying those at risk of leaving and deploying targeted interventions, reducing hiring costs and talent loss.
Source Code- https://github.com/krsubhash/Attrition-Analysis-and-Prediction
Using Long Short-Term Memory Networks (LSTM) for stock price prediction can be termed an advanced project that involves time series and deep learning. This project deals with training a model on historical price data and also testing how well this model does in predicting prices in the future.
Learning Outcomes
You will be able to practice LSTM networks and time series analysis as well as tuning their hyperparameters. Knowledge of the fundamentals of stock markets and trying out sequential data prep will also be beneficial.
Project Idea
Obtain the historical records of stock prices from any finance API and transform the data for LSTM. The LSTM model shall be trained with past prices, which is then used to forecast stock prices using metrics like RMSE for accuracy.
What It Takes to Build
Real-World Applications
Source Code- https://github.com/anubhavanand12qw/STOCK-PRICE-PREDICTION-USING-TWITTER-SENTIMENT-ANALYSIS
The movie recommendation system provides film recommendations based on the user’s preferences and past films watched. In this project, utilizing collaborative filtering or content-based filtering techniques, a model is developed that recommends based on user preferences, enhancing their engagement and satisfaction.
Learning Outcomes
This project, in turn, opens ideas in the area of recommendation algorithms, collaborative filtering, and content-based filtering methods and also works with sparse data. You will also look into the nooks and crannies of matrix factorization and the measures of similarity.
Project Idea
Create a recommendation system that makes movie suggestions based on user ratings or metadata such as director and genre using a dataset like MovieLens.
What It Takes to Build
Real-World Applications
Source Code- https://github.com/ashwinpn/Movie-Recommendation-Engines
The intermediate-level projects include sales forecasting, house price prediction, patient readmission prediction, etc. Here is the list of intermediate data analytics projects:
One of the most important aspects of marketing is customer segmentation which enables businesses to focus on people sharing certain characteristics. This project applies K-Means clustering to create customer segments based on purchase history, demographic, or behavioral attributes, which in turn allows businesses to enhance their marketing strategies and increase retention of their customers.
Project Idea
K-Means clustering is used on a customer dataset in order to divide the customers into several segments. Describe the common characteristics of each segment to help understand the types of customers and how these can assist in the efforts related to marketing.
Sales forecasting employs past databases and uses them to estimate future sales. In this project, we apply a time-series analysis with methods such as ARIMA for the data to make realistic predictions. Better sales forecast allows businesses to enhance their inventory management, human resource management, and financial management.
Project Idea
With previously spent sales, apply time series analysis to predict future sales. Prepare the data and decompose it into trend and seasonal parts, measure model performance with mean absolute error, and other such measures.
Source Code- https://github.com/akhiljamdar/Sales-forecasting-using-Time-series-analysis
Customer churn prediction is determining customers who are likely to abandon the use of a service or a product in this project. This classification project can help reduce churn rates by narrowing down on those customers who are prone to defect and developing retention mechanisms for them.
Project Idea
Create a model that forecasts a customer’s likelihood of leaving based on variables including complaints, service history, and usage. Utilize the feature engineering and evaluate using metrics like F1-score.
Source Code- https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction
A house price prediction model tries to determine the prices of a property based on real estate information such as the location, the size of the land, and the available facilities. Through the use of linear regression, this project presents a basic predictive model for estimating the prices of properties by predicting the prices of houses.
Project Idea
Create a simplistic linear regression model using a real estate dataset to assist in forecasting house prices on the basis of a focused marketing strategy. The relevant data will be preprocessed by removing or filling in missing data, and normalization, and evaluation of the model post-construction will be performed.
Source Code- https://github.com/nanditanagappa/Predicting-House-Prices-with-Linear-Regression-
Understanding patient readmission tendencies can allow hospitals to effectively plan and allocate resources to treat other patients. The goal of building this project is to estimate the readmission risk using the history and other features and demographic data of patients as well as their medical records, and this is very useful for managing patients’ health.
Project Idea
Based on the hospital admissions, obtain a data model’s prediction of when a patient is most likely to be readmitted to the hospital. Techniques such as undersampling or SMOTE must be employed for addressing data imbalance, the aim here is to draw and explain the model.
Source Code- https://github.com/moudywiyono/PatientRePro-hospital-readmission-prediction
The advanced-level projects include the Fraud Detection System, Image Classification with Convolutional Neural Networks (CNNs), Medical Diagnosis, etc. Here is the list of advanced data analytics projects:
Social media sentiment analysis informs people by analyzing the sentiments of people on the tweets by classifying them as either positive, negative, or neutral concerning the information or post. For purposes other than eye-tracking, NLP is a very effective way of analyzing tweet sentiments regarding most of the topics or brands.
Project Idea
Fetch Tweets about the specific topic using Twitter’s API. Texts are prepared and processed, features are extracted and corresponding polarities are assigned to the features by training some supervised models, for example, Naive Bayes, LSTM, and more.
Source Code- https://github.com/Lissy93/twitter-sentiment-visualisation
Interactive systems monitor and analyze outgoing financial transactions and identify fraud patterns in those transactions. This project will involve training systems to tell the difference between legitimate and fraudulent transactions which would protect against making losses.
Project Idea
You will be able to utilize a financial transactions dataset and apply classification algorithms to it to identify the presence of a fraudulent transaction. To evaluate the model performance, precision, and ROC-AUC score can be used.
Source Code- https://github.com/mrmudasir05/Bank-Fraud-Detection
Predictive maintenance is a type of predictive practice where sensor data is used to predict equipment failures so that maintenance can be performed when it is most needed Minimizing the amount of downtime. Machine learning models can estimate the time of failure, hence predicting the need for maintenance.
Project Idea
Gather sensor data from the manufacturing tools, perform data preprocessing, and employ Random Forests and LSTMs models to conduct failure forecasting among the tools. Emphasis has to be put on feature generation and building an alarm system.
Source Code- https://github.com/FaizFeroz/Predictive-Maintenance-in-Manufacturing
Training a machine learning model to identify items or categories within photos is known as image classification. Anyone working in computer vision needs to be proficient in this activity since Convolutional Neural Networks (CNNs) are especially effective at it.
Project Idea
Create a CNN model to categorize pictures from a dataset like MNIST or CIFAR-10. Execute data preprocessing procedures, specify and train the CNN architecture, and assess the correctness of the model.
Source Code- https://github.com/buseyaren/image-classification-convnets
Anomaly detection in network traffic is crucial as it identifies where abnormal and potentially nefarious activity took place. In this particular project, models are being constructed to detect anomalies based on network traffic data so as to improve alertness towards cyber hostilities.
Also Read: Top 80+ Data Analytics Interview Questions with Answers
Project Idea
Utilize unsupervised learning techniques to develop an anomaly detection model on network traffic data using models such as autoencoders.
Source Code- https://github.com/ruchira30/Anomaly-Detection-in-Network-Traffic
The 20 data analytics projects discussed above are from beginner level to advanced level and in the different fields of machine learning, time series forecasting, fraud detection, and computer vision. You will not only have a thorough grasp of data analytics techniques, but you will also be able to apply theories in practice thanks to these particular projects. The goals are that these projects will assist you in creating a stunning portfolio, enabling you to show off your abilities to future clients or employers.
If you wish to master a specific area, such as understanding customer behavior, predicting sales, or working with AI, every project opens up unique opportunities that can be used in your career or business. Working on these projects will allow you to master the concepts and advance your understanding of the field of data analytics and data science, enabling you to accept more complex challenges and pioneering projects in future times. If you’re an aspiring data analyst, consider pursuing the Certification Program in Data Analytics offered by Hero Vired in collaboration with Microsoft.
Updated on November 11, 2024
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