mrinal1704/ETL-Pipelines

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mrinal1704/ETL-Pipelines

mrinal1704

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Contains Machine learning pipeline and ETL pipeline notebooks in order to practice and learn their working.

SQL-Leetcode-Challenge

Contains all the 117 Leetcode questions with their solutions ranging from Easy to Hard in MySQL.

Credit-Card-Transaction-Fraud-Detection-using-Supervised-Machine-learning-with-an-Imbalanced-dataset

Credit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government related credit card transactions over the 2010 calendar year. The data presented a supervised problem as it included a column showing the transaction’s fraud label (whether a transaction was fraudulent or not). It also contained identifying information about each transaction such as the credit card number, merchant, merchant state, etc. The dataset had 96,753 records and 10 data fields. We first described and visualized each of the 10 data fields, cleaned the dataset, and filled in missing values. Then we created many variables and performed feature selection. Finally, we created a variety of machine learning models (both linear and nonlinear) and highlighted our results.

Jupyter Notebook

My-First-Python-Package

This repo contains the files of a python package developed by me which automates the task of getting basic insights from data.

Dog-Breed-Classifier-using-CNN

This repo contains a Dog breed classifier algorithm using deep learning. The main functions of this algorithm are that if a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling.

Jupyter Notebook

Credit-Card-Application-Fraud-Detection-using-Supervised-machine-learning-models

The provided dataset contained application (identity) fraud cases. It was a supervised problem as the data included a column showing the application’s fraud label (whether an application was fraudulent or not). It also contained several identifying data fields about the applicant such as SSN, address, phone number, etc. The dataset had 1,000,000 records and 10 data fields. We first described and visualized each of the 10 data fields and treated all frivolous values. Then we created 634 candidate variables and performed feature selection to reduce them to 30. Finally, we used a few different machine learning algorithms (both linear and nonlinear) to predict fraudulent applications records.

Jupyter Notebook

NYC-Property-Tax-Record-Fraud-Detection-using-Unsupervised-learning-models

This project analyzes New York City’s (NYC’s) real estate data to specifically identify property tax fraud. The main indicators of property tax fraud were property tax assessments that were too high or too low. Given a property dataset of 1,070,994 records and 32 data fields, we first described, visualized, and filled in missing values for each variable. Second, 45 additional variables were created in order to create the most accurate algorithm. Next, we used dimensionality reduction techniques to refine our dataset. Finally, we used (principal component analysis (PCA) and an autoencoder) to obtain two separate fraud scores. The scores were combined and then ranked to get a final fraud score.

Jupyter Notebook