Data Science
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Updated
Jul 10, 2023 - Jupyter Notebook
Data Science
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.
An analysis of house prices in Beijing
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing
🌟 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. 🚀
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.
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.
The project provides Four Tasks which is given by Cognifyz Technology.
Techniques to Explore the Data
In this notebook, i show a examples to implement imputation methods for handling missing values.
An comprehensive data analysis of a particular market and its customers.
This repository contains data analysis programs in the Python programming language.
In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.
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 pre-requisite notebooks of Data Cleaning work for my internship as a Machine Learning Application Developer at Technocolabs.
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