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Anjuum Khanna – Top 3 Machine Learning Projects for Beginners

Anjuum Khanna u2013 In the IT sector, it is more helpful to work on practical projects than theoretical knowledge. It is important to get theoretical knowledge, but in the end, this knowledge we will apply in our projects. Working on real world projects helps us with how the algorithm works, if we made a slight change to this code how it would affect the projects.

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Anjuum Khanna – Top 3 Machine Learning Projects for Beginners

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  1. Anjuum Khanna – Top 3 Machine Learning Projects for Beginners

  2. Anjuum Khanna – In the IT sector, it is more helpful to work on practical projects than theoretical knowledge. It is important to get theoretical knowledge, but in the end, this knowledge we will apply in our projects. Working on real world projects helps us with how the algorithm works, if we made a slight change to this code how it would affect the projects. In this blog post, you will discover how beginners like you can gain incredible progress in applying Machine learning to real-world problems with these awesome machine learning projects for beginners recommended by Anjuum Khanna. Top 3 machine learning projects for beginners that cover the core aspects of machine learning such as regression, unsupervised learning. In all these machine learning projects you will start with real world datasets that are freely accessible.

  3. Top 3 Machine Learning Projects for Beginners

  4. 1) Sales Forecasting using Walmart Dataset This project is available on github, created by Gagandeep Singh Khanju, This is a Regression based modelling project to forecast the sales of Walmart. This project was created on Jupyter notebook, and for this project he used the “Walmart Store Sales Forecasting” dataset, which was available on kaggle.

  5. Walmart is probably the biggest retailer worldwide and it is significant for them to have precise conjectures for their deals in different departments. Since there can be numerous components that can influence the deals for each division, it becomes basic that he distinguish the key factors that have an impact in driving the deals and use them to build up a model that can help in estimating the deals with some exactness. According to him “ In this project, he conducted multiple linear regression to predict the future sales. There were several different factors that he analyzed in his regression model starting with a full model with all the variables and then moving towards a reduced model by eliminating insignificant variables. He used several different exploratory analyses to identify the key variables for his regression equation such as correlation plots, heatmaps, histograms etc.”

  6. 2) BigMart Sales Predictions This project is available on github, created by Gurudev Aradhye. This is a Regression based modelling project which can be tried to solve using two approaches XGBoost with hypertunning and Random forest with hypertunning.

  7. This project was created on Jupyter notebook, packages which he used in the project are pandas, numpy, sklearn, matplotlib and for this project you can use the “BigMart Sales predictions” dataset, which was available on kaggle. According to him, “These two algorithms had their own importance and uses. The xgboost is used in many competitions. Here hyper tuning is performed with Greedy Search which initially takes some initial parameter values then it will search for parameter values which increases the accuracy of the model. Some details about problems are, The goal is to find item sales at Outlet of different types & located at different locations, It includes tasks such as data visualization, cleaning and engineering. transformation, feature

  8. 3) Music Recommendation system This project is available on Github, created by Sarath Sattiraju.

  9. This is a simple Music Recommendation System based on an unsupervised learning system which analyses multiple users playlists and gives recommendations for a particular playlist of a user. This model is a user-to-user based recommendation system. The dataset considered for this project is the music analysis dataset FMA. This project was created on Jupyter notebook, Clustering algorithms were used to provide predictions for the data. Recommendations were given based on the frequent genre, frequent artist, top 10 songs.

  10. About Anjuum Khanna, Tech Blogger Anjuum Khanna a strategic leader with a proven track record of over 19 years in spread heading profitable ventures within Fintech, eCom Start-ups, BPOs, Telecom & D2H, spearheaded domestic & Global Business Operations with Championed change management & enterprise wise automation initiatives within organizations in India & Middle East. Presently working as Vice President at Mswipe Technologies. large team sizes.

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