Chasing Genius

ML | PREDICTING PRICE OF PRE-OWNED CARS In this article we are going to discuss a practical application of machine learning using a case study on Linear Regression. Linear Regression is a supervised learning algorithm. It is used to predict the real- valued output y based on the given input value x. it depicts the relationship between the dependent variable y and the independent variable x. Let’s look at the problem statement: Strom Motors is an e-commerce company who act as mediators between parties interested in selling and buying pre-owned cars. Storm Motors wishes to develop an algorithm to predict the price of the cars based on various attributes associated with the car. we have downloaded the dataset for this problem statement from kaggle.com. Kaggle is an online community devoted to Data Science and Machine Learning founded by Google in 2010. The dataset is named as CAR DETAILS FROM CAR DEKHO.csv. The dataset can be found here. Data Cleaning Let us understand th...

Salary Prediction Web App In this article, we are going to discuss how to predict the salary based on various attributes related to salary  using Random Forest Regression. This study focuses on a system that predicts the salary of a candidate based on candidate’s qualifications, historical data, and work experience. This app uses a machine learning algorithm to give the result. The algorithm used is Random Forest Regression. In this problem, the target variable (or output), y, takes value of salary for a given set of input features (or inputs), X. The dataset contains gender, secondary school percentage, higher secondary school percentage, higher secondary school stream, degree percentage, degree type, work experience and specialization of candidate. Below is the step-by-step Approach: Step 1: Import the necessary modules and read the dataset we are going to use for this analysis. Below is a screenshot of the dataset we used in our analysis. Step 2: Now before moving ...

Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilised by people. Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. Supervised Learning Supervised learning works as a supervisor or teacher. Basically, In supervised learning, we teach or train the machine with labeled data (that means data is already tagged with some predefined class). Then we test our model with some unknown new set of data and predict the level of them. Types of supervised learning: Regression: Predicts continuous outcome Classification: Predicts categorical outcome. (Outcome=Yes or No, Black or White) Unsupervised Learning In unsupervised learning there would be no correct answer and no teacher for guidance. Algorithms need to discover the interesting pattern in data for learning. In my mode...