Hoax Detection Using Machine Learning

Developed a Fake News prediction model through engineering efforts. Established Random Forest as the superior model in terms of accuracy compared to other models. The project's objective is to facilitate early detection, potentially enhancing accuracy and efficiency in Fake News identification.

Human Activity Recognition Using Machine Learning

Analyzed the Human Activity datasets to identify the different activities. Implemented machine learning models, This led to increase in accuracy by 15%, affirming the effectiveness of the implemented models. Additionally , Attained Random Forest model stands out as the top performing model.

Machine Learning-driven Digital Marketing Insights

Machine Learning is profoundly impacting digital marketing predictions and similar product recommendations,in the context of similar product recommendatiomention the similar products verification system using CNN based on customer reviews, which has similar products similar to those you see online.

Breast Cancer Prediction Through Machine Learning Algorithms

Evaluated breast cancer risk, contributing to early detection and Preemptive healthcare. Employed machine learning models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Descent. Achieved Random Forest model as the most effective for the task and effectively increased

Alzheimer Detection

Data Collection,Data Preprocessing,Exploratory Data Analysis (EDA),Algorithm Selection,Model Training and Evaluation,Insights and Recommendations,and here i got better Accuracy 68.13% in-Logistic Regression,and Establish regular cognitive screening for individuals over 65ages to detect early signs.

Brain Tumor Detection Using Deep Learning

Insights into each dataset. The choice of algorithms used for detection. The results obtained from the analysis. Recommendations based on the findings.

Diabetes_Detection

By using machine learning involves analyzing medical data to develop predictive models. it includes preprocessing data to handle outliers, selecting relevant features, and training algorithms Models are evaluated using evaluation metrics such as o assess their ability to predict diabetes.

Heart Detectin Using Machine Learning

Heart detection using machine learning involves analyzing medical data to predict cardiovascular diseases. It employs algorithms to identify risk factors, assess symptoms, and aid in early diagnosis for preventive healthcare interventions.Heart Disease Detection Accuracy: 90.16% -Random Forest

Pneumonia Detection using Machine Learning

Pneumonia detection using deep learning involves training convolutional neural networks on medical images to accurately identify lung abnormalities, aiding in faster and more precise diagnosis and treatment decisions.

Covid Detection

COVID detection using ML involves training models on patient data (symptoms, tests) to classify COVID-positive or negative cases, aiding faster diagnosis and healthcare decision-making.