Cryptocurrency Price Analysis

Stock price prediction is a challenging task that involves analyzing historical stock data and developing models that can forecast future prices. This project explores various machine learning algorithms, including regression models, time series analysis, and deep learning, to predict stock prices.

Features

1. Data Visualization: Explore and visualize historical stock data to gain insights.
2. Data Preprocessing: Clean and preprocess the data for training models.
3. Model Training: Implement and train machine learning models for stock price prediction.
4. Evaluation: Evaluate the performance of the models using appropriate metrics.
5. Deployment: Deploy the trained models for real-time predictions.

Source Code


                    import matplotlib.pyplot as plt

                    import pandas as pd


                    training_set = dataset_train.iloc[:,1:2].values 

                    print('shape is ={}'.format(training_set.shape)) 

                    print(training_set[0:5])


                    class cryptcurrencyratemodel(models.Model):

                    currencytype=models.CharField(max_length=100, primary_key=True)

                                                doller=models.FloatField()

    rupee=models.FloatField()

    originalprice = models.FloatField()