profit estimation of companies with linear regression

In this Project we build fingerprint matching system that leverages a Siamese network to achieve accurate and efficient Fingerprint identification. The system consists of three main stages: image preprocessing, feature extraction, and matching.

This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.

This project analyzes the sentiment of tweets using natural language processing (NLP). It uses a dataset containing 1.6 million tweets, labeled as positive or negative, to train a machine learning model.

Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

3 modelos de classificação para analisar churn de um empresa de telecom e ao final responder a pergunta: Qual modelo teve o melhor desempenho?

A simple example of random state in train test split using python

Prepare a classification model using Naive Bayes for salary data

An insurance company called "Sure Tomorrow" wants to solve some problems with the help of machine learning. As a Data Science we're Predict the amount of insurance claims that a new client might receive and Protect clients' personal data without breaking the model with masking

Rusty Bargain is a used car buying and selling company that is developing an app to attract new buyers. My job as data science is to create a model that can determine the market value of a car.

Linear Regression Practise

Personality Recognition from text using nlp techniques

EDA Travel data by PW Skills Data Analytics Course.

A Diabetics Prediction website

Predicting The Energy Output Of Wind Turbine Based On Weather Condition DEMO LINK : https://youtu.be/ICfu49Ud2HU

Megaline company wants to develop a model that can analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra. In this classification task, we need to develop a model that is able to choose the right package

Comparative Analysis of Decision Tree Algorithms in Number Classification: Bagging vs. Random Forest vs. Gradient Boosting Decision Tree Classifiers

Run three different classification algorithms for explaining whether region's economies grew by more than 5% based on the data provided. Standard goodness measures for classification algorithms also included.

using sklearn