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.

Successfully trained a machine learning model which can predict whether a given transaction is fraud or not.

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.

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

# Data Science Salary Estimator: Project Overview * Created a tool that estimates data science salaries.

Predict Loan Eligibility for a Finance company

A Machine learning classifier to predict whether the SMS is Spam or Ham by using Natural Language Processing(NLP)

This project aims to develop a robust classification model using test-takers' demographics and questionnaire responses from the ASD screening dataset to accurately identify individuals with Autistic Spectrum Disorder (ASD) through optimization of performance metrics.

This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.