tesla_stock_prediction.py

# -*- coding: utf-8 -*-
"""Stock Prediction.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Q2USewP4-0keoFCGLggC3-QCQf95vGQH
"""

!kaggle datasets download -d rpaguirre/tesla-stock-price
!unzip tesla-stock-price.zip
!mv "Tesla.csv - Tesla.csv.csv" "Tesla.csv"

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Load data and display it in a simple plot
data = pd.read_csv('Tesla.csv', delimiter=',')
data.head()

prices = data['Close'].tolist()
dates = data.index.tolist()

plt.plot(dates, prices, color='blue', label='Actual Price', linewidth=1)
plt.title("Tesla Stock Prices (2010 - 2017)")
plt.xlabel("Date Integer")
plt.show()

# Prepare and train
X_data = np.reshape(dates, (len(dates), 1))
Y_data = np.reshape(prices, (len(prices), 1))

X_train, X_test, y_train, y_test = train_test_split(X_data, Y_data, test_size=0.25)

regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Plot data and prediction
plt.plot(dates, prices, color='blue', label='Stock History', linewidth=2)
plt.plot(X_test, regressor.predict(X_test), color='yellow', linewidth=2, label="Linear Regression Prediction")
plt.title("General Overview")
plt.legend()
plt.xlabel("Date Integer")
plt.show()