Rahul S – Medium

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Enhancing Machine Learning Projects: Strategies for Effective Data Handling and Model Performance

Machine learning has revolutionized numerous industries, from finance to healthcare, by enabling the development of intelligent systems capable of making predictions and decisions based on data. However, the success of machine learning projects relies heavily on the proper handling of data and the ability to build models that can adapt to real-world scenarios. In this essay, we will explore key aspects of data handling in machine learning, including data partitioning, bias mitigation, data leakage prevention, and addressing data drift.

Data

10 min read

Data

10 min read

15 hours ago

Python: Deep and Shallow Copy

In Python, the = (assignment) operation does not pass by reference. Instead, it binds a new name to the same object. This behavior is more accurately described as creating a reference to the object. …

Python

2 min read

Python

2 min read

Published in

Literary Impulse

·1 day ago

On Arranged Marriages

A poem on a different love

Love

1 min read

Love

1 min read

4 days ago

Python: apply(), map(), and applymap() for Data Manipulation

apply() works on both DataFrames and Series, allowing custom functions for transformation. map() is specifically for Series objects and is useful for value replacement or mapping. applymap() is applied to all elements in a DataFrame and is handy for element-wise operations. apply() apply() is a Pandas DataFrame and Series method that…

Python

2 min read

Python

2 min read

5 days ago

Recommender Systems: Collaborative Filtering and Content-Based Filtering

The article discusses recommender systems, focusing on Collaborative Filtering and Content-Based Filtering methods. Collaborative Filtering uses user interaction data, while Content-Based Filtering relies on item characteristics for personalized recommendations. Hybrid systems combine both methods for better results. — Collaborative Filtering and Content-Based Filtering are two fundamental approaches used in recommender systems to provide personalized recommendations to users. These methods are designed to address the challenges of information overload and help users discover relevant content or products.

Recommendation System

3 min read

Recommender Systems: Collaborative Filtering and Content-Based Filtering

Recommendation System

3 min read

Sep 15

Introduction to Gaussian Mixture Models (GMM) with Expectation-Maximization (EM)

1. INTUITION Imagine you have a basket of colorful marbles, and you want to group them based on their colors. But there’s a twist. You don’t know how many different colors are in the basket, and some marbles might be a mix of two or more colors. …

Machine Learning

3 min read

Introduction to Gaussian Mixture Models (GMM) with Expectation-Maximization (EM)

Machine Learning

3 min read

Sep 15

DBSCAN: Intution, Advantages, and Points to Remember

1. INTUITION Imagine you have a field of stars in the night sky, and you want to group them based on how densely they are packed together rather than a predetermined number of clusters. …

Machine Learning

3 min read

DBSCAN: Intution, Advantages, and Points to Remember

Machine Learning

3 min read

Sep 15

Recommender Systems: What goes into making one? — A Checklist

This article uncovers the key components of recommender systems, algorithms, and considerations behind their effectiveness. — Suggested Pre-Read: Recommendation Systems: An Introduction Recommendation systems are powerful tools that cater to the dynamic needs of consumers. Shoppers seek highly…aaweg-i.medium.com A good recommender system is one that provides accurate and relevant recommendations to users,

Recommender Systems

6 min read

Recommender Systems

6 min read

Sep 15

AI Strategy for Consumer Packaged Goods (CPG) with focus on Recommender Systems

In today’s fiercely competitive Consumer Packaged Goods (CPG) industry, success hinges on strategic advantage. This article explores the pivotal role of advanced CPG analytics and AI, shedding light on how these tools can empower CPG companies to drive growth, enhance efficiency, and deliver personalized experiences. Dive into the world of data-driven strategies that are reshaping the post-pandemic CPG landscape.

Artificial Intelligence

6 min read

AI Strategy for Consumer Packaged Goods (CPG) with focus on Recommender Systems

Artificial Intelligence

6 min read

Sep 15

Recommendation Systems: An Introduction

Recommendation systems are powerful tools that cater to the dynamic needs of consumers. Shoppers seek highly personalized experiences from the brands they engage with. This article explores the world of recommendation systems, delving into their functionality, advantages, real-world applications, and notable examples of companies that have harnessed their potential. What is a Recommendation System?

Marketing Analytics

3 min read

Marketing Analytics

3 min read