Data Science Minor Projects with Hands-On Learning

Gain practical experience in Data Science through beginner-friendly minor projects using real datasets. Learn data cleaning, exploratory analysis, visualization, and machine learning techniques to build strong analytical skills and prepare for real-world challenges.

Project 1: AI-Powered Resume Screening & Job Fit Predictor

Objective: To automatically screen resumes and predict how well a candidate matches a job description using NLP and machine learning.

Core Features

  • Resume text extraction (PDF/DOCX parsing)
  • Keyword and skill matching against job descriptions
  • Job fit scoring system (0–100%)
  • Model training on labeled datasets for classification
  • Dashboard to upload resumes and view scores

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, matplotlib, seaborn
  • ML Models: Logistic Regression, Random Forest, XGBoost
  • Others: PDFMiner, docx2txt for file parsing

Learning Outcomes

  • Apply NLP for text extraction and preprocessing
  • Build feature engineering pipelines for unstructured data
  • Train and evaluate classification models
  • Visualize candidate-job matching scores

 

Project 2: Hospital Readmission Risk Prediction

Objective: To predict the likelihood of a patient being readmitted within 30 days of discharge based on medical history.

Core Features

  • Data preprocessing of medical records
  • Feature selection from patient demographics and medical history
  • Classification model training for readmission risk
  • Explainable AI techniques for transparency
  • Performance monitoring dashboard for hospital use

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, imbalanced-learn
  • ML Models: Logistic Regression, Random Forest, XGBoost
  • Explainability: SHAP, LIME

Learning Outcomes

  • Build healthcare predictive analytics models
  • Apply feature engineering to medical datasets
  • Implement interpretable ML for clinical decision support
  • Evaluate models on imbalanced data

 

Project 3: Fake News Detection Using NLP

Objective: To classify online news articles as real or fake using natural language processing and machine learning.

Core Features

  • Text preprocessing (tokenization, stopword removal, lemmatization)
  • TF-IDF or Word2Vec feature extraction
  • Model training for binary classification
  • Explainability of predictions using SHAP/LIME
  • Web interface for article input and prediction

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, matplotlib, shap
  • ML Models: Logistic Regression, SVM, Random Forest, XGBoost

Learning Outcomes

  • Apply NLP for text classification tasks
  • Use vectorization techniques for feature engineering
  • Evaluate classification performance with multiple metrics
  • Implement explainable AI in NLP projects

 

Project 4: Movie Revenue Prediction

Objective: To predict a movie’s box office revenue based on features like budget, cast popularity, and release season.

Core Features

  • Data preprocessing of categorical and numerical data
  • Feature engineering for movie datasets
  • Regression model training for revenue prediction
  • Performance visualization
  • “What-if” scenario analysis for budget or cast changes

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, statsmodels
  • ML Models: Linear Regression, Ridge/Lasso, Random Forest Regressor

Learning Outcomes

  • Handle mixed feature types in regression models
  • Apply regularization to improve model generalization
  • Interpret regression coefficients for business insight
  • Build predictive analytics for entertainment industry

 

Project 5: Customer Segmentation Using RFM and Clustering

Objective: To segment customers based on their purchase behavior for targeted marketing campaigns.

Core Features

  • Calculate Recency, Frequency, and Monetary values
  • Apply clustering algorithms to segment customers
  • Visualize clusters for business interpretation
  • Create actionable marketing strategies for each segment

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, scipy
  • ML Models: K-Means Clustering, DBSCAN, Hierarchical Clustering

Learning Outcomes

  • Apply unsupervised learning for business problems
  • Understand RFM analysis for customer profiling
  • Optimize clustering parameters for better segmentation
  • Translate data insights into marketing strategies

 

Project 6: Personalized Diet Recommendation System

Objective: To recommend personalized daily meal plans based on health data and nutritional goals.

Core Features

  • User profile creation (age, weight, goals, allergies)
  • Nutritional database integration
  • Recommendation algorithms (content-based and collaborative filtering)
  • Calorie and nutrient tracking dashboard

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, surprise, matplotlib
  • ML Models: Collaborative Filtering, Content-Based Filtering, Clustering

Learning Outcomes

  • Build hybrid recommendation systems
  • Integrate nutritional data for personalization
  • Apply clustering to group similar users
  • Build health-focused predictive models

 

Project 7: Traffic Accident Severity Prediction

Objective: To classify the severity of traffic accidents based on road, weather, and location features.

Core Features

  • Data preprocessing and handling class imbalance
  • Feature engineering from date, time, and weather
  • Classification model development
  • Interactive visualization of accident hotspots

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, imbalanced-learn, xgboost
  • ML Models: Random Forest, Gradient Boosting, Logistic Regression

Learning Outcomes

  • Work with imbalanced classification problems
  • Engineer meaningful features from raw data
  • Evaluate models for public safety applications
  • Visualize spatial data for insights

 

Project 8: E-Commerce Product Recommendation Engine

Objective: To recommend relevant products to users based on browsing and purchase history.

Core Features

  • Data preprocessing of clickstream and purchase logs
  • Collaborative and content-based filtering implementation
  • Hybrid recommendation model creation
  • Real-time recommendation API

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, surprise, matplotlib, seaborn
  • ML Models: Collaborative Filtering, Content-Based Filtering, Matrix Factorization

Learning Outcomes

  • Build scalable recommendation systems
  • Integrate collaborative and content-based approaches
  • Apply matrix factorization for user-item prediction
  • Understand recommender evaluation metrics

 

Project 9: AI-Based Career Counseling Chatbot

Objective: To help students and professionals choose suitable career paths based on their skills, education, and interests using AI-based recommendations.

Core Features

  • User profile creation (skills, education, goals)
  • NLP-based chatbot interaction (local language support)
  • Skill gap analysis with suggested learning resources
  • Career roadmap generation with timelines
  • Integration with job portals for live opportunities

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, transformers
  • ML Models: Recommendation algorithms, Classification models
  • Extras: Flask/FastAPI for deployment

Learning Outcomes

  • Build AI-driven recommendation systems for career guidance
  • Apply NLP for interactive conversation systems
  • Integrate ML with real-time APIs
  • Deploy AI apps for public use

 

Project 10: Smart Traffic Flow Optimization

Objective: To reduce traffic congestion by predicting traffic flow patterns and adjusting signals dynamically.

Core Features

  • Real-time traffic data ingestion (cameras, sensors)
  • Peak hour traffic prediction
  • Dynamic traffic light adjustment algorithms
  • Route recommendations for drivers
  • Traffic heatmap visualization

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, keras, opencv, folium
  • ML Models: Time-series forecasting, CNN for image recognition

Learning Outcomes

  • Integrate IoT and ML for smart city solutions
  • Build forecasting models for transportation systems
  • Apply computer vision for vehicle detection
  • Optimize real-time traffic management

 

Project 11: Personalized Mental Health Monitoring & Suggestion System

Objective: To track and improve mental health using AI-based sentiment and activity analysis.

Core Features

  • Daily mood tracking via app input
  • Sentiment analysis of user journal/text entries
  • Stress detection based on activity patterns
  • Personalized meditation or exercise suggestions
  • Progress tracking and report generation

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, tensorflow
  • ML Models: Sentiment analysis models, Recommendation systems

Learning Outcomes

  • Apply NLP to healthcare applications
  • Build sentiment analysis pipelines
  • Implement recommendation engines for mental wellness
  • Integrate ML with user-friendly applications

 

Project 12: AI-Based Local Language Document Summarizer

Objective: To summarize government schemes, legal documents, and study material in local Indian languages using NLP.

Core Features

  • Document parsing and text cleaning
  • Summarization using extractive/abstractive techniques
  • Local language translation
  • Voice output for accessibility
  • Mobile/web interface for users

Tech Stack

  • Python Libraries: pandas, numpy, nltk, spacy, transformers, googletrans
  • ML Models: BERT-based summarization models

Learning Outcomes

  • Work with multilingual NLP models
  • Implement summarization algorithms
  • Integrate translation for local accessibility
  • Deploy NLP-based applications for public use

 

Project 13: AI-Driven Skill Gap Analyzer for Job Seekers

Objective: Identify skill gaps in a candidate’s profile compared to current job market requirements and suggest personalized learning paths.

Core Features

  • Resume parsing and skills extraction
  • Job listing scraping and skill trend analysis
  • Skill gap identification using NLP and similarity scoring
  • Course/resource recommendations from open platforms (Coursera, YouTube, etc.)
  • Progress tracking and reassessment

Tech Stack

  • Python Libraries: pandas, numpy, nltk, spacy, scikit-learn, requests, BeautifulSoup
  • ML Models: Cosine similarity, TF-IDF, Word2Vec
  • Others: Matplotlib, Seaborn for visualizations

Learning Outcomes

  • Apply NLP for resume and job description analysis
  • Implement similarity measures for skill matching
  • Integrate web scraping for live data
  • Create recommendation systems for education

 

Project 14: Food Price Prediction & Market Alert System

Objective: Predict future prices of essential food items and alert consumers about price hikes.

Core Features

  • Historical price data analysis from government portals
  • Seasonal price fluctuation detection
  • Time-series forecasting for next 30–90 days
  • Consumer notification system
  • Data visualization dashboard for markets

Tech Stack

  • Python Libraries: pandas, numpy, statsmodels, prophet, matplotlib, seaborn
  • ML Models: ARIMA, Prophet, LSTM (optional)

Learning Outcomes

  • Work with commodity price datasets
  • Implement seasonal trend detection
  • Build forecasting models for agriculture commodities
  • Develop user-friendly alert systems

 

Project 15: AI-Based Smart Study Timetable Generator

Objective: Help students optimize their study schedules based on subjects, deadlines, and personal productivity patterns.

Core Features

  • Input exam dates, subjects, and preferred study times
  • Machine learning–based focus time optimization
  • Dynamic rescheduling based on missed tasks
  • Visual calendar view and reminders
  • Study effectiveness tracking

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, flask
  • ML Models: Regression models for time optimization

Learning Outcomes

  • Apply ML for productivity optimization
  • Integrate calendar-based scheduling
  • Build adaptive algorithms that learn from user behavior
  • Create a personalized planning tool

 

Project 16: AI-Powered Local Tourism Recommendation Engine

Objective: Promote local tourism by recommending attractions, food, and events based on user preferences.

Core Features

  • User preference profiling
  • Location-based tourism suggestion system
  • Sentiment analysis of online reviews
  • Event recommendations based on season and festivals
  • Interactive map integration

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, folium
  • ML Models: Collaborative filtering, Content-based recommendation

Learning Outcomes

  • Apply recommendation algorithms to tourism data
  • Use NLP for review sentiment analysis
  • Integrate geospatial mapping for travel apps
  • Promote local economies through AI

 

Project 17: AI-Based Electricity Theft Detection

Objective: Detect anomalies in electricity consumption that may indicate theft or meter tampering.

Core Features

  • Smart meter data analysis
  • Outlier detection in consumption patterns
  • Classification of theft vs. normal usage
  • Real-time anomaly alerts
  • Dashboard for utility companies

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, pycaret
  • ML Models: Isolation Forest, One-Class SVM, XGBoost

Learning Outcomes

  • Apply anomaly detection techniques to utility data
  • Work with time-series electricity datasets
  • Build AI tools for infrastructure security
  • Implement real-time alerting

 

Project 18: AI-Powered Local Language Voice-to-Text Converter

Objective: Convert speech in Indian languages into text for accessibility and productivity tools.

Core Features

  • Multi-language audio input (Hindi, Bengali, Tamil, etc.)
  • Speech-to-text conversion using AI models
  • Punctuation and grammar correction
  • Export in multiple formats (TXT, DOCX, PDF)
  • Integration with voice assistants

Tech Stack

  • Python Libraries: speechrecognition, pyaudio, transformers, nltk, pandas
  • ML Models: Wav2Vec2, Whisper

Learning Outcomes

  • Implement speech recognition models for regional languages
  • Work with audio preprocessing techniques
  • Integrate NLP for grammar correction
  • Build accessibility tools for diverse users

 

Project 19: AI-Based Digital Farming Assistant

Objective: Assist farmers with crop planning, pest detection, and yield improvement recommendations.

Core Features

  • Crop recommendation based on soil and climate data
  • Pest detection using leaf images
  • Fertilizer usage guidance
  • Weather-based irrigation scheduling
  • Farmer-friendly mobile interface

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, tensorflow/keras, opencv
  • ML Models: CNN for pest detection, Decision Trees for crop recommendation

Learning Outcomes

  • Apply AI to agriculture decision-making
  • Integrate computer vision for plant health monitoring
  • Combine multiple ML models into one system
  • Deliver solutions for rural India under Digital India

 

Project 20: AI-Powered News Credibility Checker

Objective: Detect fake or misleading news articles using NLP and classification models.

Core Features

  • Text preprocessing and feature extraction
  • Classification into real or fake news
  • Sentiment and bias analysis
  • Source credibility scoring
  • Browser extension for instant checking

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, nltk, spacy, transformers
  • ML Models: Naive Bayes, Logistic Regression, BERT

Learning Outcomes

  • Build NLP pipelines for misinformation detection
  • Apply supervised learning to text classification
  • Integrate ML models into browser tools
  • Support ethical AI for public awareness

 

Project 21: Personalized Healthcare Chatbot for Rural Areas

Objective: Provide basic medical guidance in regional languages using AI-powered conversational systems.

Core Features

  • Symptom-based question answering
  • Multi-language support (Hindi, Marathi, Bengali, etc.)
  • Emergency health tips and nearest hospital locator
  • Offline mode with preloaded data for poor connectivity
  • Integration with government health databases

Tech Stack

  • Python Libraries: nltk, spacy, transformers, pandas, flask
  • ML Models: BERT, DistilBERT, Rasa NLU

Learning Outcomes

  • Build NLP-powered chatbots for healthcare
  • Integrate location-based services into AI apps
  • Handle multi-language datasets
  • Deliver socially impactful solutions

 

Project 22: Predictive Maintenance System for Small Factories

Objective: Predict machine breakdowns in small-scale manufacturing units to prevent losses.

Core Features

  • Sensor data collection and preprocessing
  • Anomaly detection for early warnings
  • Maintenance scheduling recommendation
  • Dashboard for tracking machine health
  • Downtime cost estimation

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, pycaret
  • ML Models: Random Forest, Gradient Boosting, Isolation Forest

Learning Outcomes

  • Work with IoT-generated time-series data
  • Apply predictive analytics for industrial problems
  • Build dashboards for decision support
  • Reduce operational losses using AI

 

Project 23: Student Dropout Prediction System

Objective: Identify students at risk of dropping out using academic, attendance, and socioeconomic data.

Core Features

  • Feature extraction from academic records
  • Classification model to predict dropout risk
  • Visualization of at-risk student clusters
  • Suggestions for retention strategies
  • Integration with school management systems

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
  • ML Models: Logistic Regression, Random Forest, XGBoost

Learning Outcomes

  • Apply ML to education data analytics
  • Build classification models with real-world datasets
  • Support decision-making in academic institutions
  • Address social issues through AI

 

Project 24: Intelligent Traffic Violation Detection System

Objective: Detect violations like signal jumping, speeding, and helmetless riding from CCTV footage.

Core Features

  • Video frame analysis using computer vision
  • Object detection for vehicles and helmets
  • Speed estimation from frame intervals
  • Automated violation logging with proof images
  • Integration with penalty systems

Tech Stack

  • Python Libraries: opencv, numpy, pandas, yolov5, tensorflow/keras
  • ML Models: YOLO Object Detection, CNNs

Learning Outcomes

  • Apply computer vision for public safety
  • Work with real-time video streams
  • Automate evidence generation for traffic police
  • Reduce manual monitoring costs

 

Project 25: AI-Powered Personalized Nutrition Planner

Objective: Recommend daily meals based on user’s health goals, allergies, and cultural preferences.

Core Features

  • Health data input and BMI calculation
  • Food database with nutritional values
  • Meal plan generation using optimization algorithms
  • Alternative food suggestions for allergies
  • Weekly grocery list generation

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, pulp, matplotlib, seaborn
  • ML Models: Optimization models, Collaborative filtering

Learning Outcomes

  • Work with optimization problems in AI
  • Apply recommendation systems for health
  • Handle user-specific constraints in algorithms
  • Build wellness-focused AI applications

 

Project 26: Predictive Analysis for University Admissions

Objective: Predict the probability of student admission based on academic scores, test results, and extracurriculars.

Core Features

  • Data cleaning and missing value handling
  • Feature selection for high impact variables
  • Binary classification for admit/reject decisions
  • ROC-AUC performance evaluation
  • Insights for improving student profiles

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
  • ML Models: Logistic Regression, Random Forest, XGBoost

Learning Outcomes

  • Handle mixed categorical and numerical data
  • Apply classification algorithms to education data
  • Evaluate model accuracy using multiple metrics
  • Provide actionable recommendations

 

Project 27: Employee Attrition Prediction System

Objective: Predict which employees are likely to leave an organization to improve retention strategies.

Core Features

  • HR data preprocessing and feature engineering
  • Binary classification model for attrition risk
  • Feature importance analysis
  • Actionable insights for HR managers
  • Attrition probability scoring

Tech Stack

  • Python Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, imbalanced-learn
  • ML Models: Logistic Regression, Random Forest, Gradient Boosting

Learning Outcomes

  • Handle imbalanced classification problems
  • Build HR analytics tools using ML
  • Interpret model results for business decisions
  • Increase employee retention through data insights

 

Project 28: House Price Prediction with Feature Engineering

Objective: Predict real estate prices using advanced feature engineering techniques.

Core Features

  • Data cleaning and outlier handling
  • Feature creation (location score, renovation age, etc.)
  • Regression model building and tuning
  • Residual analysis for model evaluation
  • Price prediction dashboard

Tech Stack

  • Python Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels
  • ML Models: Ridge/Lasso Regression, Random Forest, Gradient Boosting

Learning Outcomes

  • Apply feature engineering to improve ML performance
  • Evaluate regression models using error metrics
  • Visualize spatial and temporal property trends
  • Build decision-support tools for real estate

 

Project 29: Mental Health Sentiment Analysis on Social Media

Objective: Analyze and classify social media posts to detect mental health concerns.

Core Features

  • Data scraping from platforms like Twitter/Reddit
  • Text preprocessing and cleaning
  • Sentiment classification using NLP models
  • Trend analysis for mental health awareness
  • Visualization of emotional patterns

Tech Stack

  • Python Libraries: pandas, numpy, nltk, spacy, scikit-learn, matplotlib
  • ML Models: Naive Bayes, Logistic Regression, BERT

Learning Outcomes

  • Apply NLP for mental health detection
  • Use sentiment analysis for social causes
  • Work with unstructured text datasets
  • Develop ethical AI applications

 

Project 30: Personalized Learning Path Recommendation

Objective: Recommend learning resources based on a student’s skill level, goals, and learning style.

Core Features

  • Student profile creation with skill assessments
  • Content recommendation using collaborative filtering
  • Adaptive difficulty progression
  • Progress tracking and feedback
  • Visual learning path representation

Tech Stack

  • Python Libraries: pandas, numpy, scikit-learn, surprise, matplotlib
  • ML Models: Collaborative Filtering, Clustering, Content-Based Filtering

Learning Outcomes

  • Apply recommendation systems in education
  • Personalize learning using data-driven methods
  • Build adaptive systems for student engagement
  • Integrate ML with education platforms