Big Data Project : The goal of this project is to develop a web application based on the Apache Kafka Stream API for real-time analysis of data, with a specific focus on "predicting customer churn in real-time" for a business. Apache Kafka is a distributed event streaming platform that allows the handling of large-scale data streams efficiently.
Step 1: Real-time Data Ingestion with Apache Kafka Streams
Launch and stream real-time data from the 'customer_churn.csv' file using Apache Kafka Streams.
Step 2: Data Preprocessing with Machine Learning Libraries
Perform necessary data preprocessing using libraries such as Sklearn, PySpark MLib, or PyTorch.
Step 3: Supervised Machine Learning Training
Train supervised machine learning models (at least 3 models) on the 'customer_churn.csv' training dataset.
Step 4: Model Serialization and Storage
Save the best-performing model in .pkl format.
Step 5: Real-time Prediction using the Trained Model
Utilize the prepared, trained, and saved model to predict in real-time whether a customer will leave the institution or not based on the 'new_customers.csv' test data.
Step 6: Results Presentation with Web Application Dashboard
Present the results in the form of a web application dashboard.
Step 7: Project Upload to GitHub
Upload the entire project to GitHub for collaboration and version control.
Big Data Project : The goal of this project is to develop a web application based on the Apache Kafka Stream API for real-time analysis of data, with a specific focus on "predicting customer churn in real-time" for a business. Apache Kafka is a distributed event streaming platform that allows the handling of large-scale data streams efficiently.