This project analyzes Kaggle's Advertising Dataset to predict sales based on advertising budgets for TV, Radio, and Newspaper. Using linear regression, it explores relationships between spending and sales, evaluates model performance, and provides actionable insights for data-driven marketing decisions.

This project aimed to forecast future sales based on historical data. The objective was to build a predictive model that can assist businesses in making informed decisions about inventory and marketing strategies.

Sales prediction involves estimating future product sales based on factors like advertising spend, target audience, and platform. Businesses rely on data scientists to forecast sales and optimize advertising costs. Machine learning in Python can be used for this task.

I am currently pursuing an internship where I am honing my skills in data science and machine learning. My passion lies in uncovering insights from data and building predictive models that can drive meaningful impact.

This repository was created for my Data Science Project (Holiday Events Analysis - Walmart Sales Prediction).

This repository contains three machine learning projects: Sales Prediction, Titanic Survival Prediction, and Credit Card Fraud Detection. Full executive summaries can be accessed via google drive inside the repository.

I recently completed my five distinct tasks, as part of my data science and machine learning internship at Oasis infobyte.