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PROJECT
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.
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.
Skills
Data Science
Machine Learning Fundamentals
Data Statistics
Data Visualization
Prerequisites
Basic understanding of R language
Basic understanding of data analysis and visualisation
Basic understanding of linear and lasso regression
Technology
Rlang
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
1
Getting Started
Task 1: Load Libraries
Task 2: Load the Dataset
2
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
3
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
4
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
Congratulations!