An approach to find insights and build a machine learning of 'Employee Attrition' data in an organization using python.

This project is to develop a machine learning model and deploy it as a user-friendly web application that predicts the resale prices of flats in Singapore.

A comprehensive analysis and modelling for the Titanic data, covering, data cleaning, outlier identification, EDA, feature engineering/selection, and model evaluation. Got top 15% as a result of this effort

Election Survey Analysis by Machine Learning

The goal of this project was to develop a machine learning model that predicts customer churn based on historical customer data.

Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Working through the book R for Data Science by Hadley Wickham and Garrett Grolemund

Building a machine learning model to predict, wether the customer will get default or not

I have developed a GitHub project on ex-showroom car price prediction. The project includes data cleaning, data modeling, and data selection for accurate predictions. It also involves feature selection, model evaluation, testing, and comparison to determine the most effective model.

Insurance Forecasting with EDA, feature engineering, data preprocessing, model building and hyperparameter optimization.

Practices and Assignments from the Data Analysis and Regression Class

This provides a solution for a classification problem by determining whether the customer is suitable to be approved with a credit card or not and provides another solution using the Market Basket Analysis in order to find out combinations of items that occur together frequently in transactions.

Boston house price prediction is way to track the worth of houses in real estate.

Analyzing orthopedic data to see what hospitals to sell equipment to.

Multi-label classification is one of the standard tasks in text analytics. The objective is to perform an eXtreme multi-label classification (XMLC) on two datasets( https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) -EconBiz( ZBW - Leibniz Information Centre for Economics from July 2017) and PubMed(5th BioASQ challenge on large-…

Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier

Statistical Modelling of Swine Flu Outbreak Data

The technology used:

Predicting Hotel Booking Cancellation with Machine Learning