rfe

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

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 validity.

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

HR Analytics Dataset

The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Machine Learning Telecom Churn Model

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.

Car Price Prediction

King County House Sales

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 prospective housing properties and decide whether to invest in them or not.

Machine Learning Project

Computer Intelligence subject final project at UPC.

Build a classification model for reducing the churn rate for a telecom company

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

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

In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.

Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc. Essentially, the company wants — To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc. To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc. To know the accuracy of the model, i.e. how well these variables can predict house prices.

Predictive model that tells important factors(or features) affecting the demand for shared bikes

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.

A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state.

Regression Model using regularisation to predict the actual value of the prospective properties and decide whether to invest in them or not.

In this project we built a model to predict whether a person will remain in a hypothetical trade union called the United Data Scientists Union (UDSU).

SVM classification, RFE feature selection

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.

HDB flats resale price prediction. Neural network in Python. Machine learning models in R. Data pre-processing, feature engineering and feature selection mainly in R.

This assignment is a programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.

Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.