Sai Smruthi Reddy Kallu

• Power BI Essential Training
• Cloud Computing
• AWS Academy Cloud Foundations

Fake News Detection in USA

• Improved the accuracy of fake news detection by 15% by developing a machine learning model using Python, TensorFlow, and NLP techniques, resulting in a 85% overall accuracy rate.
• Processed and analyzed over 100,000 news articles through data cleaning and feature extraction, enhancing the model’s ability to distinguish between fake and legitimate news.
• Reduced false positives by 20% by fine-tuning the model’s hyperparameters, leading to a more reliable detection system that improves trust in content delivery platforms

Flight delay Prediction Using Machine Learning

• Enhanced data-driven decision-making by analyzing flight data and building five predictive models, identifying Random Forest Regressor as the most effective model for minimizing prediction errors.
• Reduced departure delay prediction errors by 15% by processing large datasets and optimizing the Random Forest Regressor, which achieved the lowest Mean Squared Error (2261.8) and Mean Absolute Error (24.1) among all models.
• Generated actionable insights by comparing model performance across various metrics, demonstrating that the Random Forest Regressor provided the most accurate arrival delay predictions with an MSE of 3019.3 and an MAE of 30.8.

House Price Prediction

• Developed a predictive model for housing prices by analyzing 18 key variables from a dataset of 21,613 observations, resulting in accurate price predictions to assist potential homeowners.
• Evaluated multiple modeling techniques, including Generalized Additive Models (GAM), Bagging, and Boosting, identifying the Boosting model as the best performer with 90% variance explained, improving predictive accuracy.
• Streamlined data analysis by applying Principal Component Analysis (PCA) to reduce dimensionality, enhancing model efficiency while retaining core variance and patterns in the data.