Project_01: Breast cancer prediction
Based on the different dimensions of breast model able to predict the type breast cances , by using Random forest machine learning algorithms and Hyperparameter tuning done by GridSearchCv
key skills: EDA, Random forest, Hyperparameter tuning
Project_02:Car price prediction
Selling price of car depends on the model of the car, engine type and various other factors. By using such features model able to predict the selling price of car. Here the supervised machine learning algorithm randomforest regressor used to predict the selling price of car.
key skills: EDA, Random forest regressor
Project_03: House price prediction
Selling price of house depends on how old the house is, area of the house and other features. We built a regression model to predict the elling price of the house by using such relevent features. Relevent features are selected by using feature importance from decision tree model. All Categorical features are encoded by using target giuded lebel encoding
key skills: EDA, Feature importance, Decision tree, Multiple linear regression
project_04: Flower classification
Using logistic regresiom machine learning algorithm the flowername can be predicted by using features like petal length sepal length .. etc. Since the daraset is very simple they can be sepaerable easily which can be seen by visualization. Hence logistic regression works better in case of this(irish) dataset.
Key skills: EDA, Logistic regression, Visualizaton
Proeject_05: Survival prediction(titanic dataset)
There are many people died during the incident of titanic ship sink. Among died people its unable get the data from then. To predict the survival of pasengers on the titanic ship various features used such as cab number, age ,sex and so on. Decision tree algorithms used to classify the survival or not. model works better with accuracy of 80%.
Key skills: EDA, Decision tree.