yug95 / MachineLearning

Machine learning for beginner(Data Science enthusiast)

This repository helps those who has just started or has intermediate experince in machine learning. It contains numerous examples, white papers/research papers , Blogs, implemented algorithms and business usecase documents. Our only goal is to help newcomers in datascience and hence future as well, will keep updating this repo with link papers and document related to new learning. Your feedback, comments & appreciation is always aids to improve further

Here is some of the quick link through which you can refer and detailed version listed in repo's folder-

• Hypothesis testing explanation follow medium article - https://medium.com/@yugagrawal95/hypothesis-testing-in-machine-learning-using-python-a0dc89e169ce

• K-means clustering using seaborn visualization check - https://www.kaggle.com/yugagrawal95/k-means-clustering-using-seaborn-visualization

• RFM Analysis using Python - https://www.kaggle.com/yugagrawal95/rfm-analysis

• Market Basket Analysis using Apriori - https://www.kaggle.com/yugagrawal95/market-basket-analysis-apriori-in-python

• Product Recommendation using collaborative filtering - using User-User and Item-Item approach - https://www.kaggle.com/yugagrawal95/collaborative-filtering

• Scale machine learning using pyspark on titanic dataset using pipeline - https://github.com/yug95/MachineLearning/tree/master/Spark-ml

• Deploy scale model built in pyspark using Flask webapp - https://github.com/yug95/MachineLearning/tree/master/flask_app_deployment

• White paper on Business impact of Covid-19 on Production & Manufacturing - https://www.affineanalytics.com/business-impacts-of-covid-19-on-production-manufacturing/

• rottentomatoes.com, Movie Tomatometer score Scraping code - https://www.kaggle.com/yugagrawal95/rotten-tomatoes-movie-tometometer-score-scraping?scriptVersionId=51315474

• Ad-Stock Calculation for Media Spend ( Market Mix Model ) - https://www.kaggle.com/yugagrawal95/media-ad-stock-calculation-code?scriptVersionId=51456575

• Timeseries Forecasting using SARIMA (Seasonal Arima) Method - https://www.kaggle.com/yugagrawal95/time-series-forecast-using-sarima-technique

• Timeseries Forecasting using structural time series method by Tensorflow - https://www.kaggle.com/yugagrawal95/tensorflow-strcutural-timeseries-forecast?scriptVersionId=51461325

• Timeseries Analysis using Light Gradient Boosting Machine LGBM - https://www.kaggle.com/yugagrawal95/multivariate-timeseries-analysis-by-lgbm?scriptVersionId=51462812

• Sentiment Analysis using Logistic Regression by leveraging NLTK - https://www.kaggle.com/yugagrawal95/sentiment-analysis-using-logistic-regression?scriptVersionId=52508951

• Hidden and Interesting odds of Machine learning - https://medium.com/@yugagrawal95/hidden-and-interesting-odds-of-machine-learning-759d1fd3fe26

• Marketing Mix Modelling: What Drives Your ROI? - https://medium.com/@yugagrawal95/marketing-mix-modelling-what-drives-your-roi-191402f8e683

• Price Elasticity: How Vulnerable Is Your Product In The Market? - https://medium.com/@yugagrawal95/price-elasticity-how-vulnerable-is-your-product-in-the-market-b11067b83f4a