disojn Goto Github PK

california-house-price-prediction

California House Prediction

diabetes-prediction

National Institute of Diabetes and Digestive and Kidney Diseases research creates knowledge about and treatments for the most chronic, costly, and consequential diseases. The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Build a model to accurately predict whether the patients in the dataset have diabetes or not.

healthcare-analysis

A nationwide survey of hospital costs conducted by the US Agency for Healthcare consists of hospital records of inpatient samples. The given data is restricted to the city of Wisconsin and relates to patients in the age group 0-17 years. The agency wants to analyze the data to research on the healthcare costs and their utilization. Here is a detailed description of the given dataset: AGE : Age of the patient discharged FEMALE : Binary variable that indicates if the patient is female LOS : Length of stay, in days RACE : Race of the patient (specified numerically) TOTCHG : Hospital discharge costs APRDRG : All Patient Refined Diagnosis Related Groups

loan-fraud-detection

PeerLoanKart is an NBFC(Non-banking Financial Company) that facilitates peer-to-peer loan.It connects people who need money(borrowers) with people who have money(investors). An investor would want to invest in people who showed a profile of having a high probability of paying you back.I am creating a model that will help predict whether a borrower will pay the loan or not.Here, I am trying to increase profits up to some extent as NPA (Non-Performing Asset) will be reduced due to loan disbursal for only creditworthy borrowers

natural-language-processing-nlp

(i) Spam Ham Detection (ii) Text Mining

risk-analysis-of-firms

time-series-eda-and-forecast

In this section, I begin with the excel file of sales data, which I obtained from the Tableau Community Forum. As a recall, the data contains mostly categorical variables and components of the vectors from the description column. The index column is a timeseries format. The major objective of this section is to understand the general trends in the data, and gain some quick insights, and then predict and forcast the Sales of the category "Technology" of the given sales data.The statistical significance of these observations will be also tested in 'Exploratory Data Analysis'.