Linear Regression Practise

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

Personality Recognition from text using nlp techniques

EDA Travel data by PW Skills Data Analytics Course.

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.

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

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?

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.

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

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

using sklearn

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.

Predicting sales volume at various stores

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.