Pinned Repositories
adult-income-analysis
In this project we analyze a U.S. census data taken from the UCI Machine Learning Repository. The goal of this project is to profile people in the above dataset based on available demographic attributes.
coursera-test
Coursera test repository
credit_risk_pred
Credit risk prediction using xgboost and neural network with fastAPI
Employee-salary-prediction-using-linear-regression
This project creates a data set for employees having name,age,salary and years of experience as the columns and cleaning the dataset and then predicting the salary of the particular employee
fusuma
Multitouch gestures with libinput driver on Linux
library-management-system
This is done using cpp. and has all the major fuctionalities like borrow,lend,checking avalability,displaying books list etc
MNIST-Classification-using-gridsearch-CNN
This is the python code to finetune CNN parameters for MNIST classification using gridsearch
original-bitcoin
This is a historical repository of Satoshi Nakamoto's original bitcoin sourcecode
PythonForMachineLearning
This is a repository for the Python For Machine Learning Course on xcelerator
Segmentation-of-Credit-Card-Customers
Credit Card Segmentation DATA AVAILABLE: ⮚ CC GENERAL.csv BUSINESS CONTEXT: This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Expectations from the Trainees: EXPECTATIONS: ✔ Advanced data preparation: Build an ‘enriched’ customer profile by deriving “intelligent” KPIs such as: ∙ Monthly average purchase and cash advance amount ∙ Purchases by type (one-off, installments) ∙ Average amount per purchase and cash advance transaction, ∙ Limit usage (balance to credit limit ratio), ∙ Payments to minimum payments ratio etc. ✔ Advanced reporting: Use the derived KPIs to gain insight on the customer profiles. ✔ Identification of the relationships/ affinities between services. ✔ Clustering: Apply a data reduction technique factor analysis for variable reduction technique and a clustering algorithm to reveal the behavioural segments of credit card holders ✔ Identify cluster characterisitics of the cluster using detailed profiling. ✔ Provide the strategic insights and implementation of strategies for given set of cluster characteristics DATA DICTIONARY: CUST_ID: Credit card holder ID BALANCE: Monthly average balance (based on daily balance averages) BALANCE_FREQUENCY: Ratio of last 12 months with balance PURCHASES: Total purchase amount spent during last 12 months ONEOFF_PURCHASES: Total amount of one-off purchases INSTALLMENTS_PURCHASES: Total amount of installment purchases CASH_ADVANCE: Total cash-advance amount PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase) ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency AVERAGE_PURCHASE_TRX: Average amount per purchase transaction CASH_ADVANCE_TRX: Average amount per cash-advance transaction PURCHASES_TRX: Average amount per purchase transaction CREDIT_LIMIT: Credit limit PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period MINIMUM_PAYMENTS: Total minimum payments due in the period. PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance TENURE: Number of months as a customer
Rithu722's Repositories
Rithu722/Segmentation-of-Credit-Card-Customers
Credit Card Segmentation DATA AVAILABLE: ⮚ CC GENERAL.csv BUSINESS CONTEXT: This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Expectations from the Trainees: EXPECTATIONS: ✔ Advanced data preparation: Build an ‘enriched’ customer profile by deriving “intelligent” KPIs such as: ∙ Monthly average purchase and cash advance amount ∙ Purchases by type (one-off, installments) ∙ Average amount per purchase and cash advance transaction, ∙ Limit usage (balance to credit limit ratio), ∙ Payments to minimum payments ratio etc. ✔ Advanced reporting: Use the derived KPIs to gain insight on the customer profiles. ✔ Identification of the relationships/ affinities between services. ✔ Clustering: Apply a data reduction technique factor analysis for variable reduction technique and a clustering algorithm to reveal the behavioural segments of credit card holders ✔ Identify cluster characterisitics of the cluster using detailed profiling. ✔ Provide the strategic insights and implementation of strategies for given set of cluster characteristics DATA DICTIONARY: CUST_ID: Credit card holder ID BALANCE: Monthly average balance (based on daily balance averages) BALANCE_FREQUENCY: Ratio of last 12 months with balance PURCHASES: Total purchase amount spent during last 12 months ONEOFF_PURCHASES: Total amount of one-off purchases INSTALLMENTS_PURCHASES: Total amount of installment purchases CASH_ADVANCE: Total cash-advance amount PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase) ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency AVERAGE_PURCHASE_TRX: Average amount per purchase transaction CASH_ADVANCE_TRX: Average amount per cash-advance transaction PURCHASES_TRX: Average amount per purchase transaction CREDIT_LIMIT: Credit limit PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period MINIMUM_PAYMENTS: Total minimum payments due in the period. PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance TENURE: Number of months as a customer
Rithu722/adult-income-analysis
In this project we analyze a U.S. census data taken from the UCI Machine Learning Repository. The goal of this project is to profile people in the above dataset based on available demographic attributes.
Rithu722/coursera-test
Coursera test repository
Rithu722/credit_risk_pred
Credit risk prediction using xgboost and neural network with fastAPI
Rithu722/Employee-salary-prediction-using-linear-regression
This project creates a data set for employees having name,age,salary and years of experience as the columns and cleaning the dataset and then predicting the salary of the particular employee
Rithu722/fusuma
Multitouch gestures with libinput driver on Linux
Rithu722/library-management-system
This is done using cpp. and has all the major fuctionalities like borrow,lend,checking avalability,displaying books list etc
Rithu722/MNIST-Classification-using-gridsearch-CNN
This is the python code to finetune CNN parameters for MNIST classification using gridsearch
Rithu722/original-bitcoin
This is a historical repository of Satoshi Nakamoto's original bitcoin sourcecode
Rithu722/PythonForMachineLearning
This is a repository for the Python For Machine Learning Course on xcelerator
Rithu722/Quiz-App-in-Java
This project is made using JAVA . It generates result as soon as the test is over making it paperless. It also has the report inside it
Rithu722/recommender-system
A collaborative filtering based web asset recommendation system
Rithu722/Rithu
Rithu722/Sentiment-analysis-using-SVM
In this application Support vector machine (SVM) is used to classify movie/product reviews into positive or negative.
Rithu722/Song-Recommender-Systemm
Song Recommender Systemm
Rithu722/Stock-sentiment-analysis-based-on-newsheadline
This is based on Natural Language Processing(NLP), here a model which will analyze stock price based on News Headline is created. And is analyzed based on sentiment analysis using NLP and then predict if the stock will increase or decrease. It is all about stock sentiment analysis.