Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions vali…

A multiple linear regression model for the prediction of car prices.

Machine Learning Telecom Churn Model

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

To identify the variables affecting house prices :Multiple Linear Regression in Python using statsmodels and RFE

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences

Machine Learning Project

Car Price Prediction

We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

HR Analytics Dataset

SVM classification, RFE feature selection

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Hospitals contain large databases. We can use that data to discover new useful and potentially life saving knowledge. Here we use datamining especially to predict type 2 diabetes mellitus.Predicting the percentage of chance of occurrence of Diabetes mellitus type 2 with less time complexity and high accuracy.

Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.

The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of…

Computer Intelligence subject final project at UPC.

Predict precipitation to mitigate flood damage in Bangladesh

Bike Sharing in Washington D.C.