ML Project - Linear Regression

A recommendation system created for H&M created with the help of EDA(Exploratory Data Analysis) and ALS (Alternative Least Squares) which optimizes a users recommendations taking into considerations an account`s view history and uses matrix optimization to give the best possible recommendations.

Feature Engineering

Crime and Incarceration in the United States contain data on crimes that are committed, and the prisoner counts in every 50 states, for which the data is analyzed using various analytical methods.

Xboost algorithm Model

Online Payments Fraud Detection. The objective is to predict/detect the fraud transaction that happens through online payment transactions.

Breast Cancer Detection - This project tackles the crucial challenge of early breast cancer detection using machine learning techniques. Using Machine learnig algorithms, Support Vector Machine, Randon Forest.

The goal of this problem is to predict the Price of an Old car based on the variables provided in the data set.

Hi all! My project aims to predict customer conversion for an insurance company. The main objective of the project is to develop an accurate and efficient model that can aid the insurance company in improving its sales conversion rate and reducing marketing costs.

This is a python package for the Categorical Variable Handling

Crafted a machine learning model employing Support Vector Machine (SVM) algorithm to anticipate diabetes patterns using the diabetic prediction dataset. Dive into predictive analytics with this insightful project! 📊🔍

Here we are making a predictive system to measure the sentiment of each review or tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors, and Logistic Regression are used.

To predict whether person has chronic Kidney disease or not chronic Kidney disease? • Predicted whether a patient will have chronic kidney disease or not, by using 24 predictors. • Used Decision Tree, Random Forest, XGBoost models for prediction. • Got an accuracy of 0.95 for XGBoost Model

Composed of data cleaning, random forest algorithm for future prediction.

DayCrew4 in 2023, Making PBL(Project based Learning) educational materials for beginners in data analysis.

Linear Regression on Medical Insurance Dataset

This is about Treue Technologies Data science Internship tasks.

Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV

HackerEarth Machine Learning challenge: Of Genomes And Genetics