• The explosion of data generated by various sources, including social media, IoT devices, and enterprise applications, has led to the emergence of Big Data as a critical field in data analytics. Apache Spark, specifically its PySpark module, is a powerful tool that allows for the efficient processing and analysis of large datasets in a distributed manner.These project ideas leverage PySparks capabilities to address significant challenges across various domains, including e-commerce, finance, healthcare, and social media. By employing distributed computing, machine learning, and advanced analytics, students can gain practical experience in managing and analyzing big data. Each project not only enhances technical skills but also prepares students for future careers in data science, analytics, and machine learning, making them adept at navigating the complexities of the big data landscape.

Software Tools and Technologies

• • Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
• • Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook

• • Language Version: Python 3.11.7
• • Python ML Libraries: Scikit-Learn /Numpy /Pandas /Matplotlib /Seaborn.
• • Deep Learning Frameworks: Keras /TensorFlow /PyTorch.

List Of Final Year Big Data Projects in Pyspark

• Real-Time Social Media Sentiment Analysis Using PySpark
  Project Description : This project uses PySpark to collect and process large volumes of social media data in real-time. Sentiment analysis is performed using MLlib and natural language processing techniques to track public opinion, trending topics, and brand sentiment efficiently on a distributed big data platform.
• Predictive Analytics for E-Commerce Customer Behavior Using PySpark
  Project Description : This project leverages PySpark to analyze massive e-commerce transaction datasets. Machine learning models are trained using MLlib to predict customer behavior, purchasing patterns, and product recommendations, enabling businesses to improve targeting and sales strategies.
• Fraud Detection in Financial Transactions Using PySpark
  Project Description : This project implements anomaly detection algorithms using PySpark on large-scale financial transaction datasets. Supervised and unsupervised learning techniques are applied to identify fraudulent transactions in real-time, improving security and reducing financial losses.
• Healthcare Data Analytics and Disease Prediction Using PySpark
  Project Description : This project processes large-scale patient records and medical data using PySpark. Machine learning models are applied to predict disease occurrences, analyze patient outcomes, and provide actionable insights for hospitals and healthcare providers.
• Big Data Log Analysis for Cybersecurity Using PySpark
  Project Description : This project uses PySpark to analyze large-scale network and system logs to detect cybersecurity threats. ML models and pattern recognition techniques are applied to identify anomalies, suspicious activities, and potential breaches in real-time.
• Real-Time Traffic Pattern Analysis Using PySpark Streaming
  Project Description : This project implements PySpark Streaming to process live traffic sensor data for urban traffic analysis. Real-time analytics provides insights into congestion patterns, traffic flow predictions, and route optimization for smart city planning.
• Recommendation System for Video Streaming Platforms Using PySpark
  Project Description : This project uses PySpark to build a scalable recommendation engine for video streaming platforms. Collaborative filtering, content-based filtering, and MLlib algorithms are applied on large user interaction datasets to suggest personalized content.
• Big Data Analytics for Supply Chain Optimization Using PySpark
  Project Description : This project processes massive supply chain datasets using PySpark to optimize inventory management, demand forecasting, and logistics. ML models help identify bottlenecks and improve operational efficiency.
• Energy Consumption Prediction Using Smart Meter Data and PySpark
  Project Description : This project analyzes large-scale smart meter data using PySpark to predict energy consumption patterns. Machine learning models provide insights into peak demand periods, energy optimization strategies, and grid management.
• Analyzing IoT Sensor Data for Environmental Monitoring Using PySpark
  Project Description : This project uses PySpark to process big IoT sensor datasets for environmental monitoring. Data from air quality, temperature, and pollution sensors are analyzed to detect anomalies, forecast environmental trends, and support policy-making decisions.
• Real-Time Fraud Detection in Financial Transactions Using PySpark and AI
  Project Description : This project integrates AI models with PySpark to detect fraudulent transactions in real-time. Large-scale transaction datasets are processed on a distributed framework while ML/DL models identify anomalies, unusual patterns, and potential fraud events instantly.
• Predictive Maintenance Analytics for Industrial IoT Using PySpark
  Project Description : This project processes massive IoT sensor data from industrial machines using PySpark. AI models predict equipment failures, schedule maintenance proactively, and reduce downtime, enabling smart industry operations.
• Real-Time Edge Data Analytics Using PySpark and Machine Learning
  Project Description : This project implements PySpark to process streaming edge data from IoT devices. Machine learning models perform anomaly detection, predictive analytics, and decision-making at scale, minimizing latency and improving operational efficiency.
• Big Data NLP Analytics for Customer Feedback Using PySpark and AI
  Project Description : This project uses PySpark to process massive customer feedback datasets, applying AI-driven natural language processing to extract sentiment, trends, and actionable insights for business strategy and customer satisfaction enhancement.
• Real-Time Energy Grid Optimization Using PySpark and AI Models
  Project Description : This project leverages PySpark for processing high-frequency energy consumption data from smart grids. AI models predict demand, optimize load distribution, and reduce energy wastage, enabling efficient smart grid management.
• AI-Powered Big Data Analytics for Healthcare Patient Monitoring
  Project Description : This project processes large-scale patient sensor and medical record datasets using PySpark. AI models predict disease progression, detect health anomalies in real-time, and provide actionable alerts to healthcare providers.
• Graph-Based Big Data Analysis for Social Network Insights Using PySpark
  Project Description : This project uses PySpark GraphFrames and AI algorithms to analyze massive social network datasets. Community detection, influence analysis, and trend prediction are performed at scale for marketing, social behavior, and network analysis.
• AI-Driven Big Data Cybersecurity Analytics Using PySpark
  Project Description : This project implements PySpark to process large-scale network and system logs. AI models detect anomalies, intrusion patterns, and potential cyber threats in real-time, enhancing cybersecurity monitoring and defense strategies.
• Real-Time Video Analytics Using PySpark and Deep Learning
  Project Description : This project uses PySpark to process large volumes of video data streams. Deep learning models detect objects, track movements, and identify unusual activities in real-time for applications like surveillance, traffic monitoring, and smart city management.
• Federated Big Data Analytics Across Distributed PySpark Clusters
  Project Description : This project integrates federated learning with PySpark to process distributed datasets across multiple clusters. AI models collaboratively train on local data without sharing raw information, enabling secure and scalable predictive analytics for industries and smart cities.