Data Science

This repository is on different types of data, types of missing values and how to handle missing value

Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

The project provides Four Tasks which is given by Cognifyz Technology.

An analysis of house prices in Beijing

Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.

Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing

Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features

This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.

End-to-end movie recommendation system using ML, data analysis, NLTK, CountVectorizer, cosine similarity, and TMDB API. Deployed with Streamlit.

Techniques to Explore the Data

This repository contains data analysis programs in the Python programming language.

🌟 Machine Learning Internship Cognifyz Technologies This repository highlights my work during the Machine Learning Internship at Cognifyz. It features real-world projects like restaurant rating prediction, recommendation systems, cuisine classification, and location-based analysis. 🚀

The Loan Default Analysis project aims to identify key factors contributing to loan defaults by analyzing borrower profiles, financial data, and credit risk indicators. Using statistical methods, visualizations, and predictive modeling, the project provides insights to mitigate risks and improve lending strategies.

This repository contains a project focused on data cleaning using SQL, applied to a healthcare dataset.

The Titanic classification problem involves predicting whether a passenger on the Titanic survived or not, based on various features available about each passenger. The sinking of the Titanic in 1912 is one of the most infamous maritime disasters in history, and this dataset has been widely used as a benchmark for predictive modeling.

An comprehensive data analysis of a particular market and its customers.

This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.

Implemented and compared various machine learning algorithms and visualizations on the World Population 2024 dataset to identify the most efficient predictive model. Additionally, evaluated model accuracy using different methods to ensure prediction reliability and precision.