Car Price Prediction In Python | Machine Learning

In this coding tutorial, we will perform Car Price Prediction In Python. This machine learning model helps us predict a car's price based on given features in our dataset accordingly.

Source Code: https://myprojectideas.com/car-price-prediction-machine-learning-model/

Objectives:

The objective behind building this machine learning model is to predict the price of a car so that we can get our car according to our own utility and demand balanced according to our price range. This model also helps businesses in the automobile industry to set standards to meet the requirements of the users and can also grow their businesses accordingly.

Requirements:

To build a car price prediction model using Python, you will need the following:

A dataset of car information: This dataset should include features such as make, model, year, mileage, and condition, as well as the corresponding sale price.

Python programming language: You will need to have Python installed on your computer to build the model.

Required Libraries: You will need to install libraries such as numpy, pandas, scikit-learn, matplotlib. These libraries are used in Python for data manipulation, visualization, and machine learning.

Jupyter Notebook/ IDE: You will need a development environment such as Jupyter Notebook or an IDE to write and run the code for the model.

Understanding of Machine Learning concepts and Python programming.

Before training the model, you may also need to clean the data and handle missing values and outliers.

An understanding of how to evaluate and improve the model's performance using various metrics.

Once you have these requirements in place, you can begin developing your car price prediction model using Python.


Timestamp:

00:12 - Project Overview
00:45 - Code Explanation
05:23 - Demonstration


Explanation of The Code

Initially, we imported the dataset and all the necessary libraries that were needed to build our model.

Then we checked for the null values in our dataset, and if present, we removed them accordingly.

According to our features, we have cleaned our dataset and dropped some of the columns which are not useful in our model-building process.

Then in the next section, we started our train test split phase and trained the model with Random Forest Classifier, and then with the Randomized Search CV, we selected the best number of attributes for our model building.

We have created some plots and visualizations to get insights from our dataset more concisely.

Then accordingly, we have predicted the values after the training phase is done.



#machinelearning #python #predictions