Leveraging Random Forest, Regression, and KNN for Predicting Cryptocurrency Prices

This repository contains a machine learning project focused on predicting cryptocurrency prices using various algorithms, including Random Forest, Linear Regression, and K-Nearest Neighbors (KNN).

Dataset

The dataset used in this project consists of historical cryptocurrency price data and is provided in a CSV format. You can download the dataset directly from this repository.

Download the Dataset

To download the dataset, follow these steps:

1. From GitHub Website:

   • Go to the datasets directory in this repository.
   • Click on the dataset CSV file (e.g., cryptocurrency_data.csv).
   • Once the file opens, click on the "Download" button (or right-click on "Raw" and choose "Save Link As...") to save the file to your local machine.

2. Using Git Command Line:

   • Clone the repository:

     git clone https://github.com/yourusername/crypto-price-prediction.git

   • Navigate to the datasets directory:

     cd crypto-price-prediction/datasets

   • The dataset CSV file (e.g., cryptocurrency_data.csv) will be available in the datasets directory after cloning.

Example Code to Load the Dataset

You can load the CSV dataset into your Python script using pandas as shown below:

import pandas as pd

# Load the dataset
df = pd.read_csv('datasets/cryptocurrency_data.csv')

# Display the first few rows of the dataset
print(df.head())