labelencoder

With the Student Alcohol Consumption data set, we predict high or low alcohol consumption of students.

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 will allow you to choose your labels, and then label every image in a zip file or text line in a CSV file out of the categories you chose (any text string is a valid label -NO LIMTS!)! Great for training CNN or neural network architectures of any kind!

Encoding: converting categorical data into a numerical data

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

Value to Business :: Using this Regression model, the decision-makers will able to understand the properties of various products and stores which play an important and key role in optimizing the Marketing efforts and results in increased sales.

Predictive Analytics

Feature encoding (OneHot, Ordinal, Label) and scaling (Standardization, Normalization) techniques on the Breast Cancer dataset using scikit-learn with proper train/test data leakage prevention.

CyberSoft Machine Learning 03 - Descriptive Statistics

Data Science - Neural Networks Work

End-to-end data preprocessing pipeline on the Breast Cancer dataset handling duplicates, missing values, outliers (IQR), encoding (Ordinal/OneHot/Label), scaling (MinMax/Standard), and a Logistic Regression classifier.

Trying to predict which species are most threatened with extinction in the near future.

Classification data and using ANN model

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

Dance Forms Identification: A Deep Learning Classification Problem.

Here a predictive system has made to measure the sentiment of each review or tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors, and Logistic Regression are used.

Extracted users' reviews from Amazon.com and performed sentiment analysis to determine which console to purchase

Data Preprocessing for Machine Learning

Binary classification of breast cancer using PyTorch. Used StandardScaler, LabelEncoder, Dataset, DataLoader, custom nn.Module model, BCELoss, and SGD. Focused on implementing a complete training pipeline, not optimizing accuracy.