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

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.

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

Built a Logistic Regression model in R to predict housing loan approvals using financial and demographic data. Performed data preprocessing, feature engineering, and model evaluation using ROC curve and confusion matrix. Achieved ~74% accuracy, showcasing the effectiveness of ML in loan decision-making.

An analysis of house prices in Beijing

📂 Task's and work completed during my internship at Cognifyz_Technology, focusing on Machine Learning.🧠

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

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

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.

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 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.

In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.

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

Exploratory Data Analysis - Using Python to find correlation between features