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.

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

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

The dashboard contributes some practicality to the system by ensuring the results made available and easy to comprehend even to non-technical users. Three machine learning models were evaluated: Linear Regression, Random Forest and XGBoost. One of these, Linear Regression, was obviously the best.

Exploratory data analysis and sales prediction on Diwali sales data using Python.

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.