SAJITHA A

Predictive Maintenance for Batch Reactor equipment
-Developed a machine learning model to predict maintenance needs in a batch reactor process.
-Conducted comparison analysis using historical log sheets to identify key failure patterns.
-Achieved 80% accuracy in forecasting maintenance requirements, improving operational efficiency.
Tools used:python,machine learning,
AI-Generated vs. Real Art Classification
-Implemented a deep learning model using Fast AI to distinguish AI-generated images from real artwork.
-Trained and fine-tuned the model on a curated dataset, enhancing classification performance.
-Improved accuracy through iterative model optimization, contributing to reliable image verification.
Tools used:Deep learning,FastAI library,pytorch
Viral Social Media Trends
-Exploratory Data Analysis (EDA) is performed to analyze engagement patterns.
-SQL database is used for efficient data storage and querying.
-Power BI dashboard visualizes key metrics such as views, likes, shares, and regional engagement.
-Helps identify trending hashtags, top-performing regions, and content virality.
Tools used:EDA analysis,SQL,powerBI

• EDA was performed to glean insights from several datasets.
  Built dynamic Power BI dashboards for pizza sales and Adidas.
• carried out SQL activities for data cleansing, analysis, and extraction.
• Excel dashboard was made to track Superstore sales success.
• Data retrieval was automated through the use of a Python script that connected to SQL.
  Tools used: jupyter notebook,SQL,Python,powerBI,Excel,