featurescaling

Exploratory Data Analysis

End-to-end machine learning project predicting insurance charges, with deep analysis of key cost drivers like smoking behavior, BMI, and age.

The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively…

End-to-end ML project predicting news article engagement level (Low/High) from Sri Lankan news data covering EDA, preprocessing, feature scaling, Logistic Regression modeling, and prediction on new articles.

This project analyzes customer data to understand why customers leave the company(churn pattern), identify the factors driving customer attrition, and build a Machine Learning model to predict customers likely to leave the company.

A collection of machine learning algorithms implemented for learning and practice, covering regression and classification models using Python.

END TO END ML IMPLEMENTATION

This is about Treue Technologies Data science Internship tasks.

Developed an end-to-end Credit Card Risk Analysis system using machine learning, covering data preprocessing, feature engineering, and risk classification. Built an enterprise-grade dark UI with probability-based confidence visualization and an auto-demo mode to showcase real-world fintech decision support.

Feature Scaling for Machine Learning: Understanding the Difference Btw Normalization&Standardization

Data visualization, K-value selection, feature scaling, and standardization using Python and machine learning.

In this i have performed complete feature engineering that is from handling null values, Categorical features upto performing feature scaling on our test_data and train_data.

ML using NumPy and Pandas

Using SVM to predict whether a customer can retire or not based on his/her features.

The Global Superstore dataset is a comprehensive collection of sales data spanning multiple years, regions, and product categories. This rich dataset encapsulates critical business metrics including sales revenue, profit, order quantity, and shipping cost, making it ideal for various data analysis and machine learning projects.

A machine learning project for detecting fraudulent credit card transactions using classification algorithms. Focused on handling imbalanced data and optimizing for real-world fraud detection

Analyzing and predicting life expectancy of a country based on multiple factors using multiple regression techniques

A machine learning project to predict bike rental demand using historical data. This project uses an end-to-end workflow including data preprocessing, feature engineering, and a tuned XGBoost Regressor model to achieve high predictive accuracy.

This project contains dataset of house sale prices for USA. It includes homes sold between May 2019 and May 2020. Goal to determine the market price of a house given a set of features.Analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.