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Reading the data

2 / 8

Removing rows with null data

3 / 8

Correlation between customer's annual salary and car budget

4 / 8

Average car budget by gender

5 / 8

Heat map showing the correlations between multiple variables

6 / 8

Developing the linear regression model

7 / 8

Linear regression model accuracy

8 / 8

Using the linear regression model

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Problem Statement

Understanding a customer's budget is a major challenge in the car sales industry. This is because customers have different budgets and financial situations, which can make it difficult to know which cars they can afford. This lack of understanding can lead to wasted time and resources on the part of the salesperson, as they may try to sell cars that are outside of the customer's budget. This can result in an inefficient and ineffective sales process, leading to a negative customer experience and lower sales numbers.

Solution

To address this challenge, I developed a machine learning model that accurately predicts a customer's budget based on various characteristics. This model uses linear regression and considers factors such as age, annual salary, and net worth to determine a customer's budget with a high degree of accuracy. The model was trained on a dataset of car budgets from Kaggle and achieved an accuracy of nearly 100%. This means that sales efforts can be streamlined and focused on cars that are within the customer's budget, leading to a more efficient and effective sales process.

Methodology

The methodology used to develop this machine learning solution involved several key steps. First, I conducted a thorough data analysis of the car budget dataset from Kaggle to determine which customer characteristics had the strongest impact on their budget. I found that age, annual salary, and net worth were the most important factors, and used these variables as inputs for the linear regression model. Next, I split the data into a training set (80%) and a testing set (20%) and used the training set to train the model. The model was then tested against the remaining data to evaluate its accuracy. The model performed exceptionally well, achieving near 100% accuracy when tested against the testing set.

Outcome

The outcome of this machine learning solution is a more efficient and effective sales process for the car sales industry. By providing a high degree of accuracy in predicting a customer's budget, sales efforts can be focused on cars that are within the customer's budget, leading to a better customer experience and higher sales numbers. The solution has the potential to revolutionize the way car sales are done, streamlining the process and leading to a more successful and profitable business.

To view the GitHub repository, click here: https://github.com/Brandt459/Car-Budget-Predictor