Proyek pertama predictive analytics untuk membangun model machine learning yang dapat memprediksi harga sewa rumah dan apartement di India.

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

A collection of machine learning models for predicting laptop prices

Predict laptop prices using machine learning. This project leverages multiple linear regression to achieve an 82% prediction precision. Explore the influence of features like brand, specs, and more on laptop prices.

Predicting cement strength

The objective of the project is to conduct a comprehensive analysis of a dataset of data science job postings, identifying the most important factors that influence salaries. Build predictive models that can be used to predict salaries for data science professionals, taking into account factors such as experience level, education, skills etc.

Predicting Compressive Strength of Concrete

Big Mart Sales Prediction is a data-driven project aiming to forecast product sales accurately across Big Mart outlets. Leveraging machine learning and comprehensive datasets, our project empowers retailers to optimize inventory, enhance profitability, and make informed decisions in the dynamic world of retail.

Student Performance Predictor is an end-to-end machine learning project that implements a complete predictive modeling pipeline. It analyzes the impact of demographic, socioeconomic, and academic factors on student mathematics performance, performing data preprocessing, feature engineering, regression modeling (Linear, Ridge, Lasso, Random Forest,

Developed student performance predicting model, showing strong understanding of predictive modeling techniques.

The purpose of this notebook is to develop an automated function to predict the price of a diamond based on its given features (cut, color, dimensions, etc.). We will create a machine learning model which can estimate these values. We need to find continuous data, so we will perform a regression task. We will use supervised learning to find the …

This project develops machine learning ML surrogate models to approximate the performance of a binary distillation column. Process data is generated using DWSIM, an open-source process simulator, by systematically varying key operating parameters to predict Distillate Purity and Reboiler Duty. This was my Screening Task for FOSSEE IIT-B Internship.

Student Performance Prediction Using Machine Learning