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.

profit estimation of companies with linear regression

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.

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

DrugPredictor 💊 is an AI-powered web app built with Django + Machine Learning. It predicts suitable drugs based on a patient's health details like age, sex, BP, cholesterol, and Na/K ratio. Fast, smart, and easy to use! 🚀

House Price Predictor 🏡 "A machine learning model that predicts house prices based on square footage, number of bedrooms, and bathrooms using Linear Regression. The project includes data preprocessing, model evaluation, and visualization of actual vs. predicted prices."

EDA Travel data by PW Skills Data Analytics Course.

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

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?

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

A Diabetics Prediction website

End-to-end fraud detection project using ML to identify high-risk financial transactions. Includes data cleaning, feature engineering, imbalance handling, and performance evaluation with business insights.

Linear Regression Practise

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.

Personality Recognition from text using nlp techniques

using sklearn

Predicting sales volume at various stores

The purpose of this project was to analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.

A taxi company called Sweet Lift has collected historical data on taxi orders at the airport and they need to predict the number of taxi orders for the next hour.

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.

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

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

No description provided.

Data Science Projects - beginner level.

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

Create a Machine Learning model that predicts which passengers survived the famous Titanic shipwreck.