subnil Goto Github PK

-credit-card-default

The datasets utilizes a binary variable, default on payment (Yes = 1, No = 0) in column 24, as the response variable. There are 23 features in this set: 1 Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. 2 Gender (1 = male; 2 = female). 3 Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). 4 Marital status (1 = married; 2 = single; 3 = others). 5 Age (year). 6 = the repayment status in September, 2005 7:11 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. 12 = amount of bill statement in September, 2005; 13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. 18 = amount paid in September, 2005 19 = amount paid in August, 2005 20 = amount paid in July, 2005 21 = amount paid in June, 2005 22 = amount paid in May, 2005 23 = amount paid in April, 2005

car-price-prediction-model

descriptionTools Used: R and SAS, Source: Jigsaw Academy’s Project Business Problem: Applied linear regression technique to formulate the pricing model of various car based on numerous attributes of the vehicle given. Approach: Exploratory Data analysis followed by linear regression

car-price-prediction-ridge-regression-in-python

Ridge regression technique to formulate the pricing model of various car based on numerous attributes of the vehicle given.

cardiovascular-disease-prediction-

Logistic Regression Using R

classification-of-employee-promotion-

credit-scoring-model

Tools Used: SAS, Source: Jigsaw Academy’s Project Analysed the data for 1.5 Lakh customers of the credit card company Data consisted of demographic and credit bureau variables of the customers Developed a credit scoring model (probability to default) using logistic regression Model identified the bad customers based on the risk appetite of the company

customer-loan-data-segmentation

Tools Used: SAS, Source: Jigsaw Academy’s Project Segmentation of the data set in to 8 different cluster and analyzing how one segment is different from other based on NPA status and other attributes.

dall-e_lab

This is the starting point for Lab 2 of the Dall-E course

data-visualization-forbes-most-valuable-brands

Tools Used : R Project description Read the data pertaining to “Most Valuable Brands” from Forbes website by access data using public API file and create a data frame out of it using appropriate functions and libraries.(Use the library XML, RCurl) and visualized it using ggplot2 library.

dbscan-python-code

deep-learning-regression-in-r

GREAT LAKES MACHINE LEARNING COMPETITION – PREDICT THE PRICE OF REAL ESTATE PROPERTIES IN CHENNAI

dota-2-regression-problem

Kings of Machine Learning by IIIT, Hyderabad, Analytics Vidya Hackathon- World Rank-6

house-price-prediction-model

Tools Used: R, Source: Kaggle Business Problem: With 79 explanatory variables describing every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Approach: Conducted exploratory data analysis followed by feature engineering and build advance regression model

logistic-modeling-for-snacks-manufacturer

Tools Used: R, Source: Jigsaw Academy's Case Study Developed a model for snack manufacturer’s marketing campaign design using R. This model was used to identify the critical aspect of the brand that drives the consumers’ buying behavior.

nlp-simplebot

predict-ad-clicks

Tools use: R, Source : hackerearth Machine Learning Challenge Problem Statement A leading affiliate network company from Europe wants to leverage machine learning to improve (optimise) their conversion rates and eventually their topline. Their network is spread across multiple countries in europe such as Portugal, Germany, France, Austria, Switzerland etc. Affiliate network is a form of online marketing channel where an intermediary promotes products / services and earns commission based on conversions (click or sign up). The benefit companies sees in using such affiliate channels is that, they are able to reach to audience which doesn’t exist in their marketing reach. The company wants to improve their CPC (cost per click) performance. A future insight about an ad performance will give them enough headstart to make changes (if necessary) in their upcoming CPC campaigns. In this challenge, you have to predict the probability whether an ad will get clicked or not. Approach: Exploratory Data analysis followed by clustering and logistic regression

telecom-churn-prediction-model

Analysed the data for 60,000 customers of the telecom operator Segmented the customers on various attributes and analysed their behavior segment wise Developed a model to predict churn (attrition) using logistic regression for each segment Gave recommendations to reduce the churn and increase the ARPU(Average Revenue Per User)

telecom-churn-prediction-model-using-r

Analysed the data for more than 60,000 customers of the telecom operator Segmented the customers on various attributes and analysed their behavior segment wise Developed a model to predict churn (attrition) using logistic regression for each segment Gave recommendations to reduce the churn and increase the ARPU(Average Revenue Per User)

web-scraping-of-earth-quick-data-set

Tools Use: Python, Source: Jigsaw Academy's Data Science Course Using python to query data from web to access from https://earthquake.usgs.gov ). This portal has a public API that I used to access data for all seismic events for a given duration of time. Web API.