INSAID Assignment to create a ML model to detect fraud transactions for a financial company.

This repository includes the scripts to replicate the results of my WORKING paper entitled "A Machine Learning Approach to Detect Accounting Frauds".

Classification problem using multiple ML Algorithms

Forecasting the likelihood of a customer defaulting their auto loan using classification models

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.

The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate prof…

Prevendo Customer Churn em Operadoras de Telecom

Prediction of Miles per gallon (MPG) Using Cars Dataset

Amidst the buzz of college applications, created a model to predict whether or not a student gets into a certain college based on wide range of features including test scores, univesity rankings, etc.

Multiple Regression model building with Sklearn and statsmodels and analysis of relevant predictors using P-values and VIF

Data Science - Multi Linear Regression Work

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

Logistic regression model build on lead score data to score leads on the basis of their probability of conversion.

This Jupyter notebook demonstrates a dimension reduction method by dropping high variance-inflation-factor (VIF) features recursively.

Regression models for predicting customer acquisition costs (CAC) and the effectiveness of univariate and lasso feature selection techniques in improving the accuracy.

A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc

Prepare a prediction model for profit of 50 startups data and Consider only the some columns and prepare a prediction model for predicting Price.

First project implementing Logistic Regression

Insurance charges calculation

This project uses machine learning to predict Turbine Energy Yield (TEY) from gas turbine data, optimizing settings to improve energy output, reduce fuel consumption, and cut costs. TEY predictions help detect deviations from normal operations, signaling potential turbine issues like degradation.