With this model: the amount of backlog would be reduced significantly, the amount of staff needed to do the job would be reduced drastically, the processing time would be shortened significantly and more cases of fraudulent transactions would be tracked down in a given amount of data processed - more than 40% increase in efficiency!

Over the years, the company has collected basic bank details and gathered a lot of credit-related information. The management wants to build an intelligent system to segregate the people into credit score brackets to reduce the manual efforts.. You are hired as a data scientist to build a machine learning model that can classify the credit score.

Recent times the E-commerce websites took a rapid increase in their user stats, this project is a small help for all the people out there to have a confirmation on their delivery date to avoid any issues or problems while receiving the package.

Built a Machine Learning Supervised classification algorithm, for predicting the risk of cardiovascualar disease withing coming 10 years by analyzing Patients medical History.

Ensemble multi-class classifiers on business license status data.

Machine Learning - Practical assignment

Employee Churn Prediction for a Kaggle Data Contest

Here you can explore different projects around Machine Learning Algorithms, Enjoy ☺️

ReneWind operates wind farms. Unexpected turbine failures are presenting operational and financial problems. This project uses machine learning to develop a model that accurately predict component failure, which will give the firm more control over maintenance scheduling, costs and power generation.

learning python day 15

Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data

Classification problem to predict flight delay using: Logistic Regression, Decision Tree, Random Forest, AdaBoost, Neural Network

Application to predict future occurrence of heart attack in patients on the basis of test reports

This GitHub repository contains a collection of machine learning implementations and evaluations. It includes code for ensemble learning, decision trees, AdaBoost, logistic regression, and K-means clustering. Each section focuses on a specific algorithm or technique and provides code examples for training models, making predictions, and evaluating

Diabetes is a medical disorder that affects how the body uses food for energy. When blood sugar levels rise, the pancreas releases insulin. If diabetes is not managed, blood sugar levels can rise, increasing the risk of heart attack and stroke. We used Python machine learning to forecast diabetes.

learning python day 14

In my Bangla news categorization project, I utilized XGBoost for efficient pattern recognition, SVM for handling non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic Regression to collectively enhance precision. This diverse approach ensures robust and accurate classification of Bangla news articles.

Program to predict whether a person will suffer a stroke in the near future or not with help of python

This repository contains the dataset and code for Absenteeism analysis. Many algorithms like RandomForestClassifier, SVC, AdaBoost etc.. are employed. The final model is selected based on accuracy obtained after hyper-parameter tuning the models