This project is open-ended in that we are not looking for one right answer. As John Tukey stated, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data." We want you to ask interesting questions about data and give you a chance to explore.

Business Case: Aerofit - Descriptive Statistics & Probability

Engage in the critical phase of Exploratory Data Analysis (EDA) using the tools and techniques from Python to uncover patterns, spot anomalies, test hypotheses, and identify the main structures of your dataset.

In this project, I use the 'Python Programming Language' to analyze a given dataset obtained from a shopping mall's database in a bid to increase sales by better marketing clusters.

Analyze house price data using univariate, bivariate, and multivariate analysis.

In this project, we have analyzed, explored and processed the data, developed and evaluated various classification and regression models to provide strategies for high returns with low risk for investors.

Exploratory data analysis and supervised learning analysis on the Ames Housing Prices dataset.

This project demonstrates a Clustering Model using Python. An international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It has been able to raise around $ 10 million. The model is needed to help decide ho…

This repo consists of notebooks I created to work upon the skills I learnt through several courses. All notebook has EDA, Model Building, Hyperparameter Tuning, Ensemble Models and Sampling Techniques.

The main objective of this project is to build a model to identify whether the delivery of an order will be late or on time.

Companion for the 2023 manuscript in Cancer Epidemiology, Biomarkers & Prevention entitled "Geographic Patterns in U.S. Lung Cancer Mortality and Cigarette Smoking"

Used R to analyze, explore and process the data, develop models to predict which loans are at risk of default and suggest investment strategies to the client to help them invest in P2P loans with high returns and low risk

Problem to solve: find the patterns for increased suicide rates (1985 to 2016) among different cohorts globally, across the socioeconomic spectrum, using exploratory data analysis. Using bivariate analysis, I try to determine if there is any relationship between two variables.

API backend to deploy a machine learning model to the web

A Comprehensive Guide to Titanic Machine Learning from Disaster

A bivariate sampler to generate samples from a bivariate distribution

Performed an exploratory data analysis using python and presented explanatory plots that convey insights of data.