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Price Prediction With Regression Analysis in R

In this project, we'll learn how to make predictions using multiple regression models in the R language. We'll use libraries like corrplot and glmnet to visualize data and then evaluate the models.

Price Prediction With Regression Analysis in R

You will learn to:

Load and explore data frames in R.

Create data visualisations in R.

Apply regression models on a problem.

Evaluate a model in R.


Data Science

Machine Learning Fundamentals

Data Statistics

Data Visualization


Basic understanding of R language

Basic understanding of data analysis and visualisation

Basic understanding of linear and lasso regression



Project Description

Predictive data analytics uses recorded data to predict future events through various statistical methods. Since R language centers heavily on statistics, it provides us with the right tools to create a predictive model, and offers extensive support for data visualization. It offers libraries that help apply statistical techniques like regression, time series analysis, etc., on any dataset.

The dataset that we’ll use in this project is about the specifications and prices of vehicles and is freely available on Kaggle. The vehicle’s specifications are related to its price in one way or another. The nature of the relationship can be identified and modeled via the regression model. A regression model describes and estimates the relationships among the variables and is used in the predictive analysis for a complex dataset like ours. Lasso regression is a type of regression model that makes feature selection on its own by shrinking the coefficients of insignificant variables that do not contribute to the model’s performance to zero, which makes the models easy to train.

In this project, we’ll visualize and explore the dataset and then apply regression models to the data. In the end, we’ll compare the performance of different regression models. We’ll also learn how to train, test, and evaluate models in R.

Project Tasks


Getting Started

Task 1: Load Libraries

Task 2: Load the Dataset


Exploring and Visualizing the Dataset

Task 3: Explore the Dataset

Task 4: Preprocess the Dataset

Task 5: Find Outliers in the Dataset

Task 6: Visualize Feature Correlation


Creating a Linear Regression Model

Task 7: Split the Test and Train Data

Task 8: Train the Model

Task 9: Test the Model

Task 10: Evaluate the Model's Performance


Creating a Lasso Regression Model

Task 11: Prepare the Data

Task 12: Create and Run the Lasso Regression Model

Task 13: Find the Best Lambda Value

Task 14: Build a Lasso Model With the Best Lambda

Task 15: Test the Model

Task 16: Evaluate the Model