Apple Stock Price Analysis and Prediction

This project analyzes and predicts Apple stock prices using historical data. The analysis includes data visualization and linear regression for predicting stock prices.

Project Structure

• stock market prediction Apple Inc. (AAPL).py: Python script for data analysis and visualization.
• README.md: Project documentation.

Getting Started

Prerequisites

Ensure you have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• plotly
• scikit-learn

You can install these using pip:

pip install pandas numpy matplotlib plotly scikit-learn

Running the Analysis

1. Load the Data: Read the CSV file containing Apple stock prices.

   import pandas as pd
   apple = pd.read_csv('path to your csv file')

2. Data Preprocessing: Convert the 'Date' column to datetime format and explore the dataset.

   apple['Date'] = pd.to_datetime(apple['Date'])
   print(f'dataframe contains stock prices between {apple.Date.min()} {apple.Date.max()}')
   print(f'total days {(apple.Date.max() - apple.Date.min()).days}')
   apple.describe()

3. Data Visualization: Create box plots for stock prices and line plots for the closing prices.

   import matplotlib.pyplot as plt
   import plotly.graph_objs as go
   from plotly.offline import plot, iplot
   
   apple[['Open','High','Low','Close','Adj Close']].plot(kind='box')
   
   layout = go.Layout(
       title='Stock Prices of Apple',
       xaxis=dict(
           title='Date',
           titlefont=dict(
               family='Courier New, monospace',
               size=18,
               color="#1f77b4" 
           )
       ),
       yaxis=dict(
           title='Price',
           titlefont=dict(
               family='Courier New, monospace',
               size=18,
               color="#1f77b4"  
           )
       )
   )
   apple_data = [{'x': apple['Date'], 'y': apple['Close']}]
   plot = go.Figure(data=apple_data, layout=layout)
   iplot(plot)

4. Train-Test Split: Split the data into training and testing sets.

   from sklearn.model_selection import train_test_split
   X = np.array(apple.index).reshape(-1, 1)
   Y = apple['Close']
   X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=101)

5. Standardization: Standardize the features.

   from sklearn.preprocessing import StandardScaler
   scaler = StandardScaler().fit(X_train)

6. Linear Regression: Train a linear regression model and visualize the results.

   from sklearn.linear_model import LinearRegression
   lm = LinearRegression()
   lm.fit(X_train, Y_train)
   
   trace0 = go.Scatter(
     x=X_train.T[0],
     y=Y_train,
     mode='markers',
     name='Actual'
   )
   trace1 = go.Scatter(
     x=X_train.T[0],
     y=lm.predict(X_train).T,
     mode='lines',
     name='Predicted'
   )
   apple_data = [trace0, trace1]
   layout.xaxis.title.text = 'Day'
   plot2 = go.Figure(data=apple_data, layout=layout)
   iplot(plot2)

7. Model Evaluation: Evaluate the model's performance using R² score and Mean Squared Error (MSE).

   from sklearn.metrics import mean_squared_error as mse, r2_score
   
   scores = f"""
   {'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}
   {'r2_score'.ljust(10)}{r2_score(Y_train, lm.predict(X_train))}\t{r2_score(Y_test, lm.predict(X_test))}
   {'MSE'.ljust(10)}{mse(Y_train, lm.predict(X_train))}\t{mse(Y_test, lm.predict(X_test))}
   """
   print(scores)

Results

The results section includes plots of the actual vs. predicted stock prices and the evaluation metrics of the linear regression model.