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

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

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 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.

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Data Science - Neural Networks Work

CyberSoft Machine Learning 03 - Descriptive Statistics

Predictive Analytics

Classification data and using ANN model

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

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.

Dance Forms Identification: A Deep Learning Classification Problem.

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

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.

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

Build a machine learning model to predict if a credit card application will get approved.

Classifying the genre of a music using deep neural networks.

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).

Imbalanced Classes, Resampling techniques, filling null value, Date,Time, Alphanumeric data

Project predicting geolocation from tweets as part of Yachay.ai externship

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

A collection of essential machine learning algorithms implemented from scratch and with libraries. Ideal for students and beginners to understand core ML concepts through hands-on examples.

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Data Science - Naive Bayes Work

Using supervised machine learning to predict credit risk. Trying oversampling, under sampling, combination sampling and ensemble learning to find the model with the best fit

Titanic ML competition from Kaggle platform