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azure_mlops_github_actions

book-recommandation-system

This repository contains the code for recommendation engine. This is specifically book recommendation engine.

caterpillar-tube-pricing-prediction

Caterpillar Tube Pricing Prediction

clustering-customer-data-from-travel-agency

content-based-recommendation-systems-for-e-commerce

Content based Recommendation Systems for E-Commerce

credit-card-fraud-detection

Dealing with imbalanced datasets Detect analomoies which can indicate possibly fradulant charges in credit card transaction data.It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

credit-risk-modeling

Building ML model which can help us in order get an idea, whether a person will be doing any default activity for his loan in next 2 year.

credit-risk-modelling

deep-learning-andrew-ng

forecasting-sales-with-prophet-and-time-series-analysis

Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.

human-resources-retention

We aim at building a machine learning model that can predict the employee retention rate for the HR department of a company.

m.tech_dissertation

mlops_azure

nlp

nlp-specialization

online-retail-customer-segmentation

This notebook analyzing the content of an E-commerce database. Based on this analysis, We will predict segment for customer.

predicting-insurance-premiums

The insurance.csv dataset contains 1338 observations (rows) and 7 features (columns). The dataset contains 4 numerical features (age, bmi, children and expenses) and 3 nominal features (sex, smoker and region) that were converted into factors with numerical value designated for each level.

prediction-of-signing-a-loan

Prediction Of Signing a Loan

review-summarizer

sentiment-analysis

sentiment-analysis-of-airline-tweets

telecom-churn-prediction

We have customer information for a Telecommunications company We've got customer IDs, general customer info, the servies they've subscribed too, type of contract and monthly charges. This is a historic customer information so we have a field stating whether that customer has churnded

telstra-network-disruptions-prediction

Telstra Network Disruptions Prediction

traffic-flow-prediction

The goal for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations. Specifically, the traffic volume is measured every 15 minutes at 36 sensor locations along two major highways in Northern Virginia/Washington D.C. capital region. The 47 features include: 1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), 2) week day (7 features), 3) hour of day (24 features), 4) road direction (4 features), 5) number of lanes (1 feature), and 6) name of the road (1 feature). The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With a given road network, we know the spatial connectivity between sensor locations.

who-do-we-target-for-donations-from-census-data

This is the US Census Income data set: the original data from UCI Edu. where adult.data & adult.test = 48K rows, different than 32K rows that has been mainly circulated around which also comes with many other challenges for data wrangling