Unsupervised Learning: Identify Target Customers

Linear Regression with multiple variables is implemented to predict the prices of houses using the size of the house (in square feet) and the number of rooms as features. Suppose you are selling your house and you want to know what a good market price would be.

A machine learning project on machine failure binary classification and failure type multi-class classification.

A Mathematical Intuition behind Linear Regression Algorithm

This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.

MLB Team Runs Allowed Prediction Project (Linear Regression)

Red wine quality prediction machine learning model.

Calories_Brunt_Prediction

Improving Machine Learning models performances through Feature Engineering and Feature Scaling techniques such as Principal Component Analysis (PCA), Dummy variables, Standard Scaling and Data Normalization

This is the Prediction of the student's Marks based on their Study Hours -SVM-SVR

Machine Learning in Scikit-Learn and TensorFlow

Machine learning to predict which passengers survived the Titanic shipwreck

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

Normalizes a value according to the specified steps, using feature scaling.

Supervised learning based on census data to predict income to identify potential donors

Apply unsupervised learning techniques to identify customers segments.

Using Machine Learning unsupervised learning techniques to see if any similarities exist between customers and use those similarities to segment customers into distinct categories using various clustering techniques

The purpose of this project is to analyze the impact of climate change on air quality for the city of Austin and create a machine learning model that can establish a correlation between the level of air pollutants like Ozone and NO2 and the climate parameters by using regression models and null hypothesis.

This project shows a guide for improving the accuracy of regression model.

Feature-Scaling