This project focusing on statistical analysis to understand and prepare data for potential machine learning applications. The dataset house_price.csv includes property prices in Bangalore. The analysis aims to perform exploratory data analysis (EDA), detect and handle outliers, check data distribution and normality, and analyze correlations.

Statistical data visualization with Seaborn.

Predict future sales and A/B model comparison 👾

An urban restaurant delivery service assesses its delivery performance as it looks to grow its business.

Using visualization to explore a dataset

The goal of this project is to build a predictive model to estimate the likelihood of a hospital readmission based on patient data. By identifying factors that contribute to readmissions, hospitals can optimize care and reduce costs associated with repeated visits.

Pump-up the glam in Jupyter Notebooks with innovative tools like Plotly, MatPlotLib and Seaborn.

👩🏻‍🍳🍽️Restaurant Success Prediction using ML