In this project, we aim to predict whether a particular customer will switch to another telecom provider or not, a process referred to as churning and not churning in telecom terminology.

911 Calls Capstone Project - Analysing the frequency of emergency 911 calls. This project is developed on a Jupyter notebook using Python with Numpy, Pandas data analysis libraries along with Matplotlib and seaborn data visualization libraries.

This project focuses on analyzing Diwali sales data using Python's data manipulation and visualization libraries - pandas, matplotlib, and seaborn.

Analysis of Choco-Bars groceries dataset using python data analysis tool. In this project pandas and numpy libraries are used with matplotlib.pyplot and seaborn visualization library.

This project involves a case study of a real estate company with a dataset containing property prices in the Delhi region. The goal is to optimize the sale prices of properties based on important factors such as area, bedrooms, parking, etc.

To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc.To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc.To know the accuracy of the model, i.e. how well these variables can predict house prices.

This repository contains Data Engineering projects.

Data Analytics with Python

Demonstrating how changes in input image resolution affect the algorithm's output

This is my repository where I try to create a Predictive Model in Python for NFL touchdowns.

To predict delivery time based on sorting time using simple linear regression model.

Data analysis of Instacart's grocery basket and customer profiling with evaluation of their shopping habits. (Career Foundry - Data Analytics project)

Learned techniques and tools for Knowledge Discovery and Data Mining: R, RStudio, Classification Models

Analyzed behavior event data from Southern High School, focusing on race and gender disparities.

A course project for estimating the parameters of a Weibull Distribution which models the survival rates of cancer using Maximum Likelihood Estimation

Predicting the Survival of Titanic Passengers

This project aims to analyze sales data during the Diwali festival using Python. The analysis focuses on identifying key trends, customer purchasing behavior, and sales performance across different segments. By leveraging data visualization and statistical analysis, we uncover insights.

Análisis de Datos sobre el Servicio de Internet en Argentina.

Análise Descritiva de Dados da PNAD 2015

Using Python libraries in a Jupyter Notebook, this project explores Diwali sales data, revealing valuable insights: Buyer Dynamics: Females drive sales, showcasing higher purchasing power than males. Age Impact: The 26-35 age group, primarily females, contributes significantly.Marital Influence: Married women exhibit strong purchasing potential.