A collection of notebooks for my final year project. The notebooks are used to create a virtual personal trainer to check bicep curls, squats and overhead presses.

Best for beginners | Well explained ML algorithms | organized Notebooks | Case Studies

Análisis de la fuga de empleados de una empresa con implementación de un modelo de Machine Learning para acciones de fidelización.

KNN Classifire by using Python and Jupyter NoteBooks

Scikit-learn (sklearn) projects in form of Jupyter Notebooks

I created this notebook to training my datascience skills, ssing different automatic learning models

This repository holds 2 Jupyter notebooks and one csv file on Time Series analysis for the A Yen for the Future exercises. The purpose of this code is to demonstrate understanding of time series work in Python: ARMA, ARIMA and related concepts.

This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this repository is to provide an example of a competitive analysis for those interested in getting into the field of data analytics or using python for Kaggle's Data Science competitions .

This notebook contains the sentimental analysis of the movie reviews on imdb.

Notebook created for a challenge hosted by Summer Analytics by C&A Club IITG.

A bunch of jupyter notebooks containing tensorflow and keras implementation from ANN to GANs to NLP.

A topic-wise collection of mini projects implementing basic machine learning concepts through python. Originally implemented in Google colab notebooks.

This is a very brief notebook on NLP, it contains a "Disaster Analysis" project in which all the possible architectures were shown and described briefly.

This Jupyter Notebooks is an initial study of the application of sklearn neural network MLP Classifier model. The model is applied to dataset MotorPremiums, which is supplied separately in .csv format.

This notebook is a study on the sales of newspapers of a local stand, with intention to predict the newspaper sales performance based on the different features available. For this, 4 sklearn models are applied: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net Regression.

This notebook explores and analyzes the Heart Disease UCI dataset using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn. It includes data visualization, feature engineering, model building using Random Forest Classifier, and evaluation of the model's performance in predicting the presence or absence of heart disease.

This repository is a hub for data science enthusiasts, offering a diverse collection of projects, notebooks, and resources covering topics such as data analysis, machine learning, deep learning, and generative AI. Explore innovative ideas, contribute to cutting-edge research, and enhance your skills in the dynamic field of data science

Titanic challenge part 1 In this notebook, we will be covering all of the steps required to wrangle the Titanic data set into a format that is suitable for machine learning. We will do each of the following: impute missing values create new features (feature engineering) Part 2 of this challenge involves fitting and tuning a random forest to mak…

This notebook is a study of the application of sklearn Logistic Regression model and analysis of metric quality with a focus on the impact of imbalanced data. The problem presented is the analysis of sales of newspapers of a local stand in order to classify the probability of the newspaper being Sold Out or Not, given a set of features.