Github and R Markdown, and mastery in the practice of regression analysis.

Regression and Prediction Using R
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Abstract
As part of daily life, machine learning is used to make decisions, especially by data scientists. This paper aimed to incorporate machine learning algorithms in the prediction of vehicle prices. First, the car.csv dataset was inspected, cleaned, and organized. A final dataset was arrived at and used for further analysis. The dataset was fitted with Present_Price as the response variable and the rest as explanatory variables using the basic linear model. Three algorithms, linear regression, random forest, and support vector machines, were selected for modeling. Data were partitioned into two; training and testing set. The training data was used to predict the prices on the testing set. The models' performances were evaluated and improved using tuning, cross-validation, and checked for overfitting. Lastly, the models were compared against one another using a calculated RMSE (Root Mean Squared Error). The best performing model was chosen, hence leading to the arrival of meaningful conclusions.
Keywords: Algorithms, linear regression, random forest, basic linear model, dataset, regression, and output.
Introduction
Every day, applications for machine learning are typical to come across. The algorithms help in making critical decisions in every field of work. For instance, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations, and retailers use it to gain insight into their customers' purchasing behavior. Closer to home, data scientists using machine learning to advise on future data patterns and behavior that could be encouraged or discouraged. Besides, it entails building models that offer predictive power and can be used to understand data not yet collected.
In earlier statistics classes, machine learning has been used when running simple regression models. On the other hand, this is a complex topic with a wide range of possibilities and applications. Therefore, this study sought to present a basic understanding of regression modeling using linear, random forest, and support vector modeling, as well as to answer the following questions of interest;
* What is the relationship among variables, especially between the car price variable with other variables?
* Is it possible to predict the price of a new car based on historical data?
* Which is the best model for use in prediction among the three?
Data Description
The car dataset contained information about cars and motorcycles listed on CarDekho.com. The car data was in a CSV file and included the following columns: model/ Car_Name, year, selling price, showroom price/Present_Price, kilometers driven, fuel type, seller type, transmission, and the number of previous owners. Using R, the dataset had nine columns and 301 rows/observations. After cleaning the dataset, there were four numerical and five categorical variables. Those categories were model, fuel type, seller type, and transmission. Categorical ones were namely, year, selling prices, showroom price, kilometers driven, and the number of previous owners. As instructed, an additional data point was added for a 2018 manual city selling at 4.34M but presen...