The "Advertising Impact Analysis" project aims to analyze the relationship between advertising expenditure across different channels (such as TV, radio, online) and its impact on sales or revenue.

A demonstration of exploartory data analysis with hypothesis testing.

Used cars price prediction using Python, Machine Learning, HTML and CSS.

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

Utilizando-se a técnica de regressão linear, com o auxílio dos frameworks scikit-learn e statsmodel, foi possível criar um modelo de predição de preços de imóveis, com base em variáveis explanatórias de um database.

Utilizando-se a técnica de regressão linear, com o auxílio do framework scikit-learn, foram realizados dois projetos nos quais foram utilizados dois databases diferentes (um de consumo de cerveja, e outro do preço de imóveis). Utlizando-se ambos, foi possível prever o consumo de cerveja e o preço dos imóveis, com base nas variáveis explanatórias.

Explore the complete lifecycle of a machine learning project focused on regression. This repository covers data acquisition, preprocessing, and training with Linear Regression, Decision Tree Regression, and Random Forest Regression models. Evaluate and compare models using R2 score. Ideal for learning and implementing regression use cases.

Linear_Regression_Practical_Salary

This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.

Intrusion Detection System for MQTT Enabled IoT.

Vehicle price prediction using linear, lasso and elastic net regression algorithms.

In the digital music era, understanding artist popularity on Spotify is vital. This project taps into Spotify's data, analyzing key factors driving artist prominence. Through our insights, we illuminate what sets successful artists apart in this dynamic platform.

Beta Bank is losing customers monthly. Employees want to focus on client retention. As a Data Scientist, I created a model to predict the chance of a customer leaving, based on past behavior and contract terminations.

A project showcasing the various steps involved in carrying out a basic linear regression task for prediction of a target variable.

A Preprocessing Modelling Study using Supervised ML Models

Time Series Forecasting - Bus Usage Prediction

To predict people's quality of life