Help in correcting code for predicting stock market prices (the plotted graph is weird)

Hey everyone, this is my code for predicting stock market prices using SVR. I am using the historical data from But for some reason the historical prices are appearing weirdly in the graph. Can anyone help rectify this?

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

companies = ['AAPL Historical Data', 'GOOGL Historical Data', 'MSFT Historical Data', 'NVDA Historical Data', 'IBM Historical Data']
predictions = {}

for company in companies:
    # Load the company stock data
    df = pd.read_csv(f'{company}.csv')
    df = df.sort_values('Date')

    # Calculate the number of days from the start
    df['Date'] = pd.to_datetime(df['Date'])
    df['Date'] = (df['Date'] - df['Date'].min()).dt.days

    # Prepare the features (Date) and target (Close price)
    X = df['Date'].values.reshape(-1,1)
    y = df['Price'].values

    # Standardize the features and target
    scaler_X = StandardScaler().fit(X)
    scaler_y = StandardScaler().fit(y.reshape(-1,1))
    X = scaler_X.transform(X)
    y = scaler_y.transform(y.reshape(-1, 1)).ravel()

    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create and train the Support Vector Machine (Regressor) 
    svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1), y_train)

    # Test the model
    svr_rbf_confidence = svr_rbf.score(X_test, y_test)
    print(f"svr_rbf confidence for {company}: ", svr_rbf_confidence)

    # Predict the future stock prices (Next 6 months, 30 days/month)
    x_forecast = np.array(list(range(max(df['Date']), max(df['Date'])+180))).reshape(-1,1)
    x_forecast = scaler_X.transform(x_forecast)
    forecast_result = svr_rbf.predict(x_forecast)
    forecast_result = scaler_y.inverse_transform(forecast_result)
    predictions[company] = forecast_result

    # Plot the prediction
    plt.plot(df['Date'].values, df['Price'].values, color='blue', label='Historical Close price')
    plt.plot(list(range(max(df['Date']), max(df['Date'])+180)), forecast_result, color='red', label='Future Close price')
    plt.title(f'{company} Stock Price Prediction')
    plt.xlabel('Days from start')
    plt.ylabel('Stock Price (Price)')

The results I get are:

and so on for the other 4 companies
I am not allowed to upload more than 1 image but hope y’all can understand what is wrong

The points are plotted in the order they appear in the underlying data given to Matplotlib. Sort them by date first.

Alternatively, use a scatter plot (scatter) rather than a line plot (the default plot)