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.

This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. …

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.

Strategic Analysis and Insights: Enhancing Netflix Content Strategy for Global Growth

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.

The goal of this project is to predict the future medical expenses of patients based on certain features. Factors affecting the medical expenses of the patients - age, gender, body mass index, region, smoking behavior, medical health expenses.

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

Analyzing Demand Drivers for Yulu's Micro-Mobility: A Hypothesis Testing Study

Data Exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest.

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.

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

making analysis using Exploratory and Explanatory visualizations through Univariate, Bivariate, Multivariate Exploration