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.

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

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

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

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.

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

Data Science - Multi Linear Regression Work

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.

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

This project predicts stock price of Infosys using machine learning. It involves data collection, data preprocessing, feature engineering, model building, hyperparameter tuning and model evaluation.

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

R programming - Statistical Modelling II

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.