- Breast Cancer Prediction
- Description: The project predicts the diagnosis (M = malignant, B = benign) of the Breast Cancer
- Technologies Used: The notebooks uses Decision Tree Classification and Logistic Regression
- Results: The logistic regression gave 97% accuracy and decision tree gave 93.5% accuracy
- Red Wine Quality Prediction(
- Description: The project predicts the quality of the wine in the value 0 or 1. 1 for good quality and 0 for bad quality
- Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
- Results: The logistic regression model performs the best with accuracy of 86.67%
- Heart Stroke Prediction
- Description: The project predicts the risk of heart stroke on studying the person's demographics and medical info
- Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
- Results: The logistic regression, SVM and KNN performs the best with 93.8 % accuracy
- House Price Prediction
- Description: The project predicts the house price after studying the variables such as location, area, bredroom, bathroom count and many more.
- Technologies Used: The notebooks uses Linear Regression, Ridge Regression and Random Forest Regressor
- Results: The Random Forest Regressor performed best with accuracy of 87.89%
- Titanic Survival Prediction
- Description: The project predicts the survival during the titanic disaster based on socio-economic measures
- Technologies Used: The notebooks uses Descision Tree Classifier
- Results: The Decision Tree Classifer performed well on the test dataset with an accuracy of 89.5%
- Diamond Price Prediction
- Description: The project predicts the price (in US dollars) of the diamonds based on their features
- Technologies Used: The notebooks uses Descision Tree Regressor and Random Forest Regressor
- Results: The Decision Tree Regresor performed well on the test dataset with an accuracy of 96%
- Medical Cost Prediction
- Description: The project predicts the medical treatment cost by analysing the patients age, gender, bmi, smoking habits etc.
- Technologies Used: The notebooks uses Linear and Polynomial Regression, Decision Tree and Random Forest Regressor
- Results: The Decision Tree Regressor and Random Forest Regressor performed well
- Room Occupancy Detection
- Description: The project predicts the room occupancy by analyzing the sensor data such as temperature, light and co2 level.
- Technologies Used: The notebooks uses Random Forest Classifier
- Results: The Random Forest Classifier performed well with an accuracy of 98%
- Sleep Disorder Prediction
- Description: The project aims to predict sleep disorders and their types by analyzing lifestyle and medical variables, such as age, BMI, sleep duration, blood pressure, and more
- Technologies Used: The notebooks uses Random Forest Classifier and Decision Tree cLassifier
- Results: The Random Forest Classifier performed well with an accuracy of 89%
- Pima Indians Diabetes Prediction
- Description: The primary objective of the Pima Indian Diabetes Prediction project is to analyze various medical factors of female patients, to predict whether they have diabetes or not.
- Technologies Used: The notebooks uses Logistic Regression, Random Forest Classifier and Support Vector Machine
- Results: The Logistic Regression performed with an accuracy of 78%.
- Bank Customer Churn Prediction
- Description: The main objective of the Bank Customer Churn Prediction project is to analyze the demographics in order to predict whether a customer will leave the bank or not.
- Technologies Used: The notebooks uses Random Forest Classifier and Decision Tree Classifier
- Results: The Random Forest Classifier and Decision Tree Classifier performed equally well with an accuracy of 87%
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