Exploratory Data Analysis of the mpg dataset is examining and understanding the characteristics of the data to uncover patterns and relationships within the dataset.
Machine learning project
Analyze Diwali Sales data using Pandas, NumPy, Matplotlib, and Seaborn Libraries to Improve customer experience and also sales.
Analysis of dataset of a Delivery Center located in Brazil. Transformation, visualization and regression of orders, drivers, payments, stores variables.
A machine learning datathon by DataFest Africa
X Education Organization wants to identify if a customer registered on their website for enquiry is a potential customer or not. Using past data to build a machine learning algorithm
Prediksi customer churn penting bagi perusahaan mengingat biaya yang diperlukan untuk menarik pelanggan baru lebih mahal dibandingkan mempertahankan pelanggan lama sehingga prediksi customer churn memberikan kemudahan bagi perusahaan memberikan treatment tertentu untuk mempertahankan customer tersebut
Analyzing and Visualizing the evolution of Smartphone specifications on a web-scraped dataset.
Ongoing Project: "Transplant wait time prediction" IPBA, IIM Indore.
Extraction Cleaning Manipulation Visualization Machine Learning & More
This repo has extensive work I have done on Pandas and NumPy Modules during the advanced programming Module
This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status
**(Open to Collaboration):** This project evaluates the impact of discounts on sales and customer retention for Eniac. It includes data cleaning, visualization, storytelling, and strategic insights to optimize discount strategies while maintaining brand reputation. 📊🛍️✨
explore the market capitalization of different cryptocurrencies.
DataScience Task for final acceptance stage for eT3 Internship
Data Cleaning and Analysis - A part of Assessment 2 -GD604 Data Collection and Analysis New Zealand School of Education College (NZSE)
Analyze Zomato restaurant data all the world and find the insights by using Python libaries and also visualize the dataset by using Power-bi
Using Python Pandas to clean and transform an anonymized version of a dataset.
An exploratory analysis on various exercises. This analysis explores factors such as Difficulty Level, Target Muscle Group, Primary Equipment, Posture, and Body Region. for helping the development of Home Workout Application
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