Top 5 Data Science Capstone Project Ideas That Will Impress Employers and Sharpen Your Skills

📗 Chapter 4: Stock Market Forecasting with Time Series Models

Predict Market Trends Using Time Series Analysis & Machine Learning

🧠 Introduction

Stock market prediction is one of the most challenging and rewarding applications of data science. Investors, analysts, and trading platforms rely on time series forecasting models to anticipate stock price movements and make informed decisions.

This capstone project teaches you how to analyze, visualize, and forecast stock price trends using Python, time series models like ARIMA and Prophet, and key financial indicators.

You’ll walk through:

• Time series preprocessing
• Exploratory data analysis
• Feature engineering (rolling averages, returns, volatility)
• Forecasting using ARIMA, Prophet
• Model evaluation and visualization

🎯 Objective

Goal: Predict the closing price of a stock (e.g., Apple Inc. - AAPL) for the next N days using historical stock data.

📊 Step 1: Load Historical Stock Data

We’ll use the Yahoo Finance API via yfinance to fetch real-time data.

python

!pip install yfinance

import yfinance as yf

import pandas as pd

# Load historical data

stock = yf.download("AAPL", start="2015-01-01", end="2023-12-31")

stock = stock[['Open', 'High', 'Low', 'Close', 'Volume']]

stock.head()

🧼 Step 2: Preprocessing & Feature Engineering

Convert index to datetime

python

stock.index = pd.to_datetime(stock.index)

stock = stock.asfreq('B')  # Fill business days

stock = stock.fillna(method='ffill')

Add moving averages & returns

python

stock['SMA_20'] = stock['Close'].rolling(window=20).mean()

stock['SMA_50'] = stock['Close'].rolling(window=50).mean()

stock['Daily_Return'] = stock['Close'].pct_change()

stock['Volatility'] = stock['Close'].rolling(window=20).std()

📈 Step 3: Visualize Trends

python

import matplotlib.pyplot as plt

plt.figure(figsize=(14, 6))

stock['Close'].plot(label='Close')

stock['SMA_20'].plot(label='20-day SMA')

stock['SMA_50'].plot(label='50-day SMA')

plt.title('Apple Stock Price with Moving Averages')

plt.legend()

plt.show()

🔁 Step 4: Time Series Decomposition

python

from statsmodels.tsa.seasonal import seasonal_decompose

result = seasonal_decompose(stock['Close'], model='multiplicative', period=252)

result.plot()

plt.show()

🤖 Step 5: Forecasting with ARIMA

Split data

python

from statsmodels.tsa.arima.model import ARIMA

train = stock['Close'][:-100]

test = stock['Close'][-100:]

Fit ARIMA

python

model = ARIMA(train, order=(5,1,0))

model_fit = model.fit()

forecast = model_fit.forecast(steps=100)

# Evaluate

from sklearn.metrics import mean_squared_error

import numpy as np

mse = mean_squared_error(test, forecast)

print(f"ARIMA RMSE: {np.sqrt(mse):.2f}")

Plot forecast

python

plt.figure(figsize=(12,6))

plt.plot(test.index, test, label='Actual')

plt.plot(test.index, forecast, label='ARIMA Forecast')

plt.legend()

plt.title("ARIMA Model Forecast vs Actual")

plt.show()

📅 Step 6: Forecasting with Facebook Prophet

python

!pip install prophet

from prophet import Prophet

df_prophet = stock.reset_index()[['Date', 'Close']]

df_prophet.columns = ['ds', 'y']

model = Prophet()

model.fit(df_prophet)

future = model.make_future_dataframe(periods=30)

forecast = model.predict(future)

model.plot(forecast)

model.plot_components(forecast)

🔍 Step 7: Add Lag Features & Train ML Model (Advanced)

python

stock['Lag_1'] = stock['Close'].shift(1)

stock['Lag_2'] = stock['Close'].shift(2)

stock.dropna(inplace=True)

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import train_test_split

X = stock[['Lag_1', 'Lag_2', 'SMA_20', 'SMA_50', 'Volatility']]

y = stock['Close']

X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, test_size=0.2)

rf = RandomForestRegressor(n_estimators=100)

rf.fit(X_train, y_train)

y_pred = rf.predict(X_test)

print(f"Random Forest RMSE: {np.sqrt(mean_squared_error(y_test, y_pred)):.2f}")

📊 Summary of Results

💡 Deployment Ideas

• Streamlit App: Input stock ticker → forecast 30 days
• Plotly Dash App: Interactive charts
• Jupyter Dashboard: Embed multiple models and comparisons