This repository is a collection of basic code templates for Data Preparation. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.

Code in which an initial approach to decision trees and bagging will be made, and an attempt will be made to ensure that the model can be trained with any dataset coming from Kaggle (for this, we will again use the 'connect with Kaggle' project).

This repository is totally focused on Feature Engineering Concepts in detail, I hope you'll find it helpful.

This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.

This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..

This is a machine learning project which implements three different types of regression techniques and formulates differences amongst them by predicting the price of a house based on Boston housing Data.

Poland Bankruptcy Prediction (2009) This project aims to predict whether a Polish company went bankrupt in 2009 based on its financial data. The dataset contains several features derived from companies' balance sheets, and the goal is to build models that can identify bankruptcy effectively — despite the challenge of high class imbalance.

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.

This project predicts whether a person survived the Titanic disaster based on various features using machine learning. It utilizes pipelines, ColumnTransformer, and model serialization for efficient processing and prediction.

Real-Fake-Job-Post

complete case analysis drops the whole column if there are missing values, arbitrary value imputation in this we can use replace (mean or median) with -1 or 99.999, end of the distribution it replaces the values with "missing" term

pipelines chains together multiple steps so that the output of each step is used as input to the next step

while we load the dataset we get some missing values from dataset. so to replace the missing values we use a technique in Machine Learning called Imputation. Imputation --- 1. SimpleImputer 2.KNNImputer

This project is aimed at predicting the likelihood of coronary heart disease (CHD) in individuals over the next ten years using Logistic Regression.