oktaykurt / price-prediction-with-machine-learning

Price Prediction with Machine Learning

This repository contains machine learning models for predicting prices using various machine learning techniques.

Folder Structure

• Linear Regression: This directory contains Jupyter notebooks implementing linear regression models.
• Support Vector Regression: This directory contains Jupyter notebooks implementing support vector regression models.
• Decision Tree Regression: This directory contains Jupyter notebooks implementing decision tree regression models.
• Random Forest Regression: This directory contains Jupyter notebooks implementing random forest regression models.

Each directory contains multiple notebooks that apply the respective machine learning model to different datasets or configurations.

Getting Started

To run the notebooks, you'll need Jupyter and the following Python libraries: pandas, numpy, matplotlib, seaborn, and sklearn. Install them using pip:

pip install jupyter pandas numpy matplotlib seaborn sklearn

Then, clone this repository, navigate to the desired directory, and launch Jupyter:

git clone https://github.com/oktaykurt/price-prediction-with-machine-learning.git

cd price-prediction-with-machine-learning/Linear\ Regression

jupyter notebook

Open the desired notebook in your browser.

Usage

Each notebook contains code cells that you can run one by one. They follow this general structure:

1. Import libraries: Import the necessary Python libraries.
2. Load the dataset: Load a CSV or Excel file into a pandas DataFrame.
3. Preprocess the data: Clean the data and prepare it for the machine learning model.
4. Train the model: Use a scikit-learn model (like LinearRegression or SVR) to train the model on the data.
5. Evaluate the model: Evaluate the model's performance using metrics like mean squared error.

Remember to replace the dataset path with the correct path to the dataset on your machine.