Website sources for Applied Machine Learning for Tabular Data

Code for the CUP Elements on text analysis in Python for social scientists

Lead Scoring is such a powerful metric when it comes to quantifying the lead & it is nowadays used by every CRM. In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models.

MachineShop: R package of models and tools for machine learning

This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

Framework to evaluate Trajectory Classification Algorithms

A scikit-learn compatible hyperbox-based machine learning library in Python

En este proyecto de GitHhub podrás encontrar parte del material que utilizo para impartir las clases de Introducción a la Ciencia de Datos (Data Science) con Python.

No description provided.

This project aims to analyze and classify a real network traffic dataset to detect malicious/benign traffic records. It compares and tunes the performance of several Machine Learning algorithms to maintain the highest accuracy and lowest False Positive/Negative rates.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine

A tool to support using classification models in low-power and microcontroller-based embedded systems.

Sentiment analysis of Tokopedia app users on Google PlayStore using the Support Vector Machine (SVM) method

Projet-PI-4DS2

MetaPerceptron: A Standardized Framework For Metaheuristic-Driven Multi-layer Perceptron Optimization

A repository dedicated to storing guided projects completed while learning data science concepts with Dataquest.

Repository for several data science and analysis projects

In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.

A graphical machine learning program written with tkinter and scikit-learn library.

Machine learning system for predicting genetic disorders using genomic, clinical, and demographic data. Implements robust preprocessing, feature selection, and multi-model classification (RF, XGBoost, LightGBM, CatBoost) with cross-validation to support early, data-driven genetic risk assessment.

Neuronal morphology preparation and classification using Machine Learning.

Successfully established a machine learning model which can predict whether any given water sample is potable or not, based on its set of various properties, to a considerably high level of accuracy.

This aims to explore different data mining techniques, such as classification, regression, clustering, and association rule mining, using datasets available in Weka.

In simpler words we tell whether a user on Social Networking site after clicking the ad’s displayed on the website,end’s up buying the product or not. This could be really helpful for the company selling the product. Lets say that its a car company which has paid the social networking site(For simplicity we’ll assume its Facebook from now on)to display ads of its newly launched car.Now since the company relies heavily on the success of its newly launched car it would leave no stone unturned while trying to advertise the car. Well then whats better than advertising it on the most popular platform right now.But what if we only advertise it to the correct crowd.

Basics of classification and bias

This is a customer churn prediction project using machine learning algorithms like Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost, and Gradient Boosting. The project aims to analyze and predict customer churn in a dataset, using techniques like class weighting and SMOTE to handle class imbalance

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

Based on the Udemy Course "Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]"

A repository housing a CNN model for text recognition, implemented in Python with TensorFlow and Keras.