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a-study-on-statistical-techniques-for-credit-scoring

This project aims to build a credit scorecard using machine learning techniques in Python. The credit scorecard is a common industry problem used to assess the credibility of a customer for various financial services, including credit cards, loans, and other financial products.

abc-call-volume-trend-analysis

The attached dataset is of Inbound calls of an ABC company from the insurance category consists of a Customer Experience (CX) Inbound calling team for 23 days. Data includes Agent_Name, Agent_ID, Queue_Time, Time, Time_Bucket , Duration [duration for which a customer and executives are on call, Call Seconds , call status .

assumptions-of-linear-regression

bank_loan_case_study

In this case study, apart from applying the techniques that you have learnt in the EDA module, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers.

credit--risk-analytics

credit-risk--pd-lgd-ead-el

credut-risk-pd-lgd-ead-el

END TO END CREDIT RISK MODEL

cultivation-of-tea-coffee-in-india

I have studied the trends of these variables have been studied & different time series models, methods (moving average, trend curve fitting) have been made. I got the study variable and also the predicting equation which will help to do forecasting of any year.

eda_imdb_movies

we are providing you with dataset having various columns of different IMDB Movies. You are required to Frame the problem. For this task, you will need to define a problem you want to shed some light on.

employee-review-analysis

end-to-end-house-price-prediction

The main objectives of this study are as follows: - To apply data preprocessing and preparation techniques in order to obtain clean data - To build machine learning models able to predict house price based on house features - To analyze and compare models performance in order to choose the best model

end-to-end-project-on-building-a-credit-scorecard-using-statistical-models

This project aims to build a credit scorecard using machine learning techniques in Python. The credit scorecard is a common industry problem used to assess the credibility of a customer for various financial services

energy-efficiency

The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses.

hiring-process-analysis

Hiring process is the fundamental and the most important function of a company. Here, the MNCs get to know about the major underlying trends about the hiring process.

house-price-prediction-using-decision-tree-random-forest

, I'll train decision trees and random forests to predict the price of a house using information like its location, area, no. of rooms etc. I'll use the dataset from the House Prices - Advanced Regression Techniques competition on Kaggle.

house-price-prediction-using-linear-regression-

I'm going to predict the price of a house using information like its location, area, no. of rooms etc. I'll use the dataset from the House Prices - Advanced Regression Techniques competition on Kaggle.

instagram-project-analysis-sql-project

User analysis is the process by which we track how users engage and interact with our digital product (software or mobile application) in an attempt to derive business insights for marketing, product & development teams. These insights are then used by teams across the business to launch a new marketing campaign, decide on features to build for an

insurance_persistancy_prediction_final

1. Problem Description: Prepare a Machine Learning Model to predict the Persistency 13M Payment Behaviour at the New Business stage. ## 2. Objective: Using Machine Learning techniques, provide scores for each policy at the New Business stage the likelihood to pay the 13M premium.

marketing-stratagy-

medical-insurance-cost-prediction

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!

operation-analytics-investigating-metric-spikes

Operation Analytics is the analysis done for the complete end to end operations of a company. With the help of this, the company then finds the areas on which it must improve upon.

optimal-product-price-determination

p-7_-python-data-structures-boolean

p1-regression-analysis

Linear Regression, Polynomial Regression , Multiple Regression On Salary ,Cars And 50 start ups Data Set . Dummy Variable Encoding Is Also Here.

p2-decision-tree

Decision Tree Algorithm

p3-k-means-clustering-on-mall-data-set

Applying EDA & K-means Clustering On Mall Dataset

p4_k-means_clustering-on-wine-clustwering-dataset

worked with wine clustering dataset here . Performed EDA , principal component analysis , K-means clustering

p5-regression-analysis_-in-depth

Here we have applied in depth regression analysis using life expectancy data and cloud seeding data set .

p6-heteroskedasticity-checking

Here I have checked and removed for heteroskedasticity .

portfolio-optimization