A New Interactive Approach to Learning Data Analysis
Case study to identify risky loan applicants and understand factors that contribute to a loan default.
The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.
This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project
Udacity Data Analyst Nanodegree - Project V
Exploratory Data Analysis Theory and Python Code
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This repository contains Exploratory Data Analysis in Python on Autism Behavioural Challenges on children(0-18 years) dataset
Summer Training on Machine Learning by Internshala, powered by Analytics Vidhya,
Exploratory Data Analysis on IPL data (2008 - 2018). Finding patterns in data
Case study to understand driving factors (or driver variables) behind loan default.
This module will cover the role SQL in the world of data. It also introduces you to basic mathematical and graphical techniques to analyze data.
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. …
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.
Descriptive Statistics 2023/2024
EDA analysis on loan dataset from Lending Club Case Study to understand the driving factors (or driver variables) behind loan default
Predicting Cryptocurrency Prices with Machine Learning - Time Series Forecasting
Explore data related to credit card defaulters to identify customer personas. Discover patterns in the data and interpret how each feature impacts the target variabl
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