This is a prediction of car prices using Linear Regression. A simple Univariate Linear Regression
Introduction: This project involves predicting the prices of cars using Linear Regression, specifically for this project I have used Univariate Linear Regression. Linear Regression is a statistical method that is used to predict dependencies between variables. It is a very useful tool in data science and machine learning, and it can be used to predict prices of cars based on several factors such as year, make, model, and other relevant features.
Project Description: The main goal of this project is to predict the prices of cars using a simple linear regression model. The dataset contains car prices and various attributes such as make, model, year, mileage, and body style of the car. We will use these attributes to build a model that can accurately predict car prices.
The dataset used in this project is a cleaned and preprocessed version of the original dataset. The data has been cleaned by removing missing values, duplicate rows, and irrelevant columns. The dataset contains information on over 300 cars and their prices.
Prerequisites: To run this project, you will need the following:
- Python 3.x
- Python Libraries: pandas, numpy, matplotlib, seaborn, sklearn
Installation: You can install the required packages by running the following command in your terminal:
pip install pandas numpy matplotlib seaborn sklearn
Usage: To run the project, simply clone the repository and run the following Python script:
python car_price_prediction.py
This will run the script and output the predicted car prices.
Conclusion: This project shows how Linear Regression can be used to predict the prices of cars based on various factors. The Linear Regression model can be further optimized and improved by tweaking the various parameters and hyperparameters. Overall, this project serves as a great introduction to the world of predictive analysis using Linear Regression.