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

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

A dataset of meteorological sensors with data cleaning and finding the best learning model for forecasting

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.

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

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

Data Science - Multi Linear Regression Work

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

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.

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

📗 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.

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

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

Analyze & visualize U.S. crime data + advanced ML to predict 3-year recidivism risk.

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.