The LEED Predictor project is designed to forecast the likelihood of buildings achieving LEED (Leadership in Energy and Environmental Design)
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Updated
Jul 10, 2024 - Jupyter Notebook
The LEED Predictor project is designed to forecast the likelihood of buildings achieving LEED (Leadership in Energy and Environmental Design)
Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
Building a machine learning model to check bank frauds
An assessment of logistic regression model performance in predicting customer loan worthiness using loan size, interest rate, borrower income, debt-to-income ratio, number of credit accounts, derogatory marks, and total debt as predictive factors.
Spam messages detection model
In this Classification Machine Learning Project, we will be analysing the dataset taken from www.kaggle.com related to details of credit card owners. This data consists of features like Gender, Income type, House type, marital status and many more. Our focus will be on analysing the data, getting the insights related to these features and there …
Player Rating System in Soccer using Machine Learning
This project is based on supervised machine learning where you will be predicting whether a credit card transaction is original transaction or fraud transaction based on various parameters. This is a classification problem.
Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.
Credit Card Fraud Detection
Classification of Fraudulent Transactions in Mobile Based Payments
Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.
It is a NLP project.The business objective was to predict whether the message is a spam or a ham.
Machine Learning projects
This project provides a comprehensive analysis of the Eurovision Song Contest, with insights derived from both traditional statistical methods and machine learning techniques.
This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.
Predict if a transaction is a fraud transaction or not, also, dealing with unbalanced data and finding the pattern using correlation between the features.
Beginner friendly project focusing on dataset imbalances using the oversampling and under sampling techniques
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