Projects in Machine Learning from Beginner to Advanced.
Project 1. Rock vs Mine Prediction:
A system coded in Python that can predict whether an object is either Rock or Mine with SONAR data. In this use case, we are utilising a logistic regression model for our prediction.
Project 2. Diabetes Prediction
A project can predict a woman's diabetes status using machine learning. This project makes use of Python. In this study, we use a support vector machine model for the prediction. A prediction method can detect whether a woman is diabetic or not based on dataset features including pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree, and age.
Project 3. Fake News Prediction
A project on predicting fake news using machine learning and Python. This project uses a dataset with features like id, title, author, and text to predict if a news is fake or real. We will be using Logistic Regression model for prediction
Project 4. Loan Status Prediction
A model that can predict the loan status of person by his personal details like Gender, Married, Dependents(kids), Education, Self Employed, Applicant Income, Coapplicant Income, Loan Amount, Loan Amount Term, Credit History and Property Area. We are using support vector machine model for the prediction.
Project 5. House Price Prediction
A model that can predict the House Price by house details like Id, MSSubClass(Identifies the type of dwelling involved in the sale), MSZoning(Identifies the general zoning classification of the sale), LotArea(Lot size in square feet), LotConfig(Configuration of the lot), BldgType(Type of dwelling), OverallCond(Rates the overall condition of the house), YearBuilt(Original construction year), YearRemodAdd(Remodel date (same as construction date if no remodeling or additions)), Exterior1st(Exterior covering on house), BsmtFinSF2(Type 2 finished square feet), TotalBsmtSF(Total square feet of basement area), SalePrice(To be predicted) which are given in the boston dataset. It is a Regression Problem which we are solving using XGBoost model.
Project 6. Wine Quality Prediction
A model that can predict Wine Quality by its features like fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol and quality. We are using support vector machine model for the prediction.