manoj-nain Goto Github PK

aichamp

Codvector AIChamp Screening tasks

aichamp1

Weekly Tasks for AIChamp

amazon-scraper

A simple web scraper to extract Product Data and Pricing from Amazon

autoscraper

A Smart, Automatic, Fast and Lightweight Web Scraper for Python

cancellation-prediction

Predicting hotel cancellations with machine learning

code

Compilation of R and Python programming codes

credit-default-risk-analysis-and-its-detection-using-machine-learning

IndNatBank is a peer to peer loan financial company who provides loan to its potential customers all over the India. They make profit based on the risk they issues loans to the borrowers. Based on the previous data they want to analyze the risk of issuing loans to the new customers which at the same time also helps to improve the personalization user experience while applying for loans. They have experienced employees who uses the complex rules to provide services to their customers. But as the size of data will increase their traditional ways of assessing risk might not be good for the company. They wants to automate their process by which machine learns the pattern out of their data for better customer experience. Now the question is how this problem could be solved using Machine Learning? While there are many ways to assess the credit loan risk and its depiction. We will be working with a simple scenario to solve this problem.

eda-on-macdonal-s-dataset

Exploratory data analysis on MacDonald's Dataset

finance-factoring-using-machine-learning

In this project we will do EDA on historical data of a Finance Factoring company and build ML Models to predict the duration of any invoice getting paid.

fraudulent-transaction-analysis-and-detection-using-machine-learning

In this project we will do analysis on transactions of a payment company to detect the fraud using ML models. Let's say a company named as IPAYU has been providing it's financial services to the variety of users in a country. But at the same time there are loopholes in security measures (in the meanwhile company is upgrading it's security). Fraudster might try to crack inside the company's interface and can commit fraudulent transactions. It may harm people trust towards the company. To tackle this situation of crisis, they have consulted a team of data scientists. Now the question is how this problem could be solved using data science? While there are many ways to prevent the fraud and loss occur. Let's walk through a simple pave how this team handled problem of fraudulent transaction to identify and automated model for future transactions.

game_control

getsure-control-game-opencv

Gesture controlled Hill Climb Racing game using OpenCV, Python

glassdoor-review-scraper

Scrape reviews from Glassdoor

gooey

Turn (almost) any Python command line program into a full GUI application with one line

hello-world

sample repository

home-mortgage-analysis

In this project we will do EDA on Home Mortgage Disclosure Act (HMDA) dataset to analyze and predict the mortgage decision and create Machine Learning models to predict the same for future.

invisible-cloak

invoicenet

Deep neural network to extract intelligent information from PDF invoice documents.

jupyter-text2code

A proof-of-concept jupyter extension which converts english queries into relevant python code

mastering-apache-spark

This is repository of my YouTube Course on End to End Apache Spark in AIEngineering YouTube Channel

mobile-app-data-set-analysis

Analysis of Google Play store and IOS App store Data sets

opencv

Computer vision using OpenCV and Python

pandas-bokeh

Bokeh Plotting Backend for Pandas and GeoPandas

pdf2audiobook

pdf2audiobook

proxy_requests

a class that uses scraped proxies to make http GET/POST requests (Python requests)

pythoncode-tutorials

The Python Code Tutorials

quant-trading

Python quantitative trading strategies including Pattern Recognition, CTA, Monte Carlo, Options Straddle, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

refrence_notebooks

OpenCV and Deep Learning Reference Notebooks

regulatory-compliance-analysis

In this project we will do analysis of consumer complaints and find out how companies follows the regulatory norms. We will categorize the complaints according to product type and analyse which industry faces most issues in compliance. Then we will make ML model to categorize future data.

retail-analytics

EDA on Retail data and Creating ML models for profit prediction on each product