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

Encoding: converting categorical data into a numerical data

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.

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!

Data Preprocessing for Machine Learning

Data Science - Neural Networks Work

Classification data and using ANN model

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.

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

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

Predictive Analytics

Dance Forms Identification: A Deep Learning Classification Problem.

Module 13 - I am creating a binary classification model using a deep neural network by preprocessing data for a neural network model , using the model-fit-predict pattern to compile and evaluate a binary classification model , and optimize the model.

A spam email chacking system using the Complement-Naive-Bayes algorithm on SpamAssassin datasets

CyberSoft Machine Learning 03 - Descriptive Statistics

In this project we built a model to predict whether a person will remain in a hypothetical trade union called the United Data Scientists Union (UDSU).

Classifying the genre of a music using deep neural networks.

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

Here we are making a predictive system 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.