Stock Price Prediction using Machine Learning in Python

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Stock Price Prediction using Machine Learning in Python

MACHINE LEARNING:proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not.

In this article, we will learn how to predict a signal that indicates whether buying a particular stock will be helpful or not by using ML.

Let’s start by importing some libraries which will be used for various purposes which will be explained later in this article.

Importing Libraries

Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.

Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
Matplotlib/Seaborn – This library is used to draw visualizations.
Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
XGBoost – This contains the eXtreme Gradient Boosting machine learning algorithm which is one of the algorithms w
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn import metrics
import warnings
warnings.filterwarnings('ignore')

Importing Dataset

The dataset we will use here to perform the analysis and build a predictive model is Tesla Stock Price data. We will use OHLC(‘Open’, ‘High’, ‘Low’, ‘Close’) data from 1st January 2010 to 31st December 2017 which is for 8 years for the Tesla stocks.

df = pd.read_csv('/content/Tesla.csv')

df.head()

Exploratory Data Analysis

is an approach to analyzing the data using visual techniques. It is used to discover trends, and patterns, or to check assumptions with the help of statistical summaries and graphical representations.

While performing the EDA of the Tesla Stock Price data we will analyze how prices of the stock have moved over the period of time and how the end of the quarters affects the prices of the stock.