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Stock Price Prediction Project

Rumman Ansari June 26, 2026 10 views Subject Details

TIME SERIES PROJECT

Stock Price Prediction Project

Build a powerful AI-based Stock Price Prediction model using Python, LSTM, and Time Series techniques.

Project Overview

In this hands-on project, you’ll build a Stock Price Prediction System using LSTM (Deep Learning). The goal is to predict the future stock prices based on historical patterns using Time Series Analysis.

Goal: Predict the next closing stock price using past data.

Project Objectives

Understand Time Series & Stock Data.
Preprocess and visualize historical stock data.
Build an LSTM Deep Learning model.
Train and evaluate predictions.
Forecast future stock prices.

Prerequisites

Required Tools:

Python 3.x
Jupyter Notebook
Libraries: pandas, numpy, matplotlib, scikit-learn, tensorflow, yfinance

pip install pandas numpy matplotlib scikit-learn tensorflow yfinance

Project Workflow

Step-by-Step Plan

Import libraries
Download stock data
Visualize stock trends
Preprocess data
Build LSTM model
Train and evaluate
Forecast future prices
Visualize results

Step 1 — Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

Step 2 — Download Stock Data

df = yf.download("AAPL", start="2018-01-01", end="2024-12-31")
df = df[["Close"]]
print(df.head())

Output Downloads Apple Inc. (AAPL) stock data from Yahoo Finance.

Step 3 — Visualize Stock Price

df["Close"].plot(title="AAPL Stock Closing Price", figsize=(10,4))
plt.xlabel("Date")
plt.ylabel("Price")
plt.show()

Step 4 — Preprocess Data

scaler = MinMaxScaler(feature_range=(0,1))
data_scaled = scaler.fit_transform(df)

# Prepare time series sequences
def create_sequences(data, seq_length=60):
    X, y = [], []
    for i in range(seq_length, len(data)):
        X.append(data[i-seq_length:i, 0])
        y.append(data[i, 0])
    return np.array(X), np.array(y)

X, y = create_sequences(data_scaled, 60)
X = np.reshape(X, (X.shape[0], X.shape[1], 1))

Each input has the past 60 days, and we predict the next day's closing price.

Step 5 — Build LSTM Model

model = Sequential([
    LSTM(64, return_sequences=True, input_shape=(60, 1)),
    Dropout(0.2),
    LSTM(64, return_sequences=False),
    Dropout(0.2),
    Dense(32, activation="relu"),
    Dense(1)
])

model.compile(optimizer="adam", loss="mean_squared_error")
model.summary()

Output Builds a deep LSTM neural network for time series forecasting.

Step 6 — Train the Model

model.fit(X, y, epochs=20, batch_size=32)

Step 7 — Predict Stock Prices

predicted = model.predict(X)
predicted = scaler.inverse_transform(predicted)
real_prices = scaler.inverse_transform(y.reshape(-1, 1))

plt.figure(figsize=(12,5))
plt.plot(real_prices, label="Actual Price")
plt.plot(predicted, label="Predicted Price")
plt.title("Stock Price Prediction")
plt.legend()
plt.show()

Output Plots predicted prices vs. real stock prices.

Step 8 — Forecast Future Stock Prices

last_60 = data_scaled[-60:]
future_input = np.reshape(last_60, (1, 60, 1))
future_prediction = model.predict(future_input)
future_prediction = scaler.inverse_transform(future_prediction)
print("Next Day Predicted Price:", future_prediction[0][0])

Output Predicts the next day's expected closing price.

Complete Project Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

# Download data
df = yf.download("AAPL", start="2018-01-01", end="2024-12-31")[["Close"]]

# Scale data
scaler = MinMaxScaler(feature_range=(0,1))
data_scaled = scaler.fit_transform(df)

# Create sequences
def create_sequences(data, seq_length=60):
    X, y = [], []
    for i in range(seq_length, len(data)):
        X.append(data[i-seq_length:i, 0])
        y.append(data[i, 0])
    return np.array(X), np.array(y)

X, y = create_sequences(data_scaled, 60)
X = np.reshape(X, (X.shape[0], X.shape[1], 1))

# Build LSTM Model
model = Sequential([
    LSTM(64, return_sequences=True, input_shape=(60,1)),
    Dropout(0.2),
    LSTM(64, return_sequences=False),
    Dropout(0.2),
    Dense(32, activation="relu"),
    Dense(1)
])
model.compile(optimizer="adam", loss="mean_squared_error")

# Train model
model.fit(X, y, epochs=20, batch_size=32)

# Predict and visualize
predicted = scaler.inverse_transform(model.predict(X))
real_prices = scaler.inverse_transform(y.reshape(-1,1))

plt.plot(real_prices, label="Actual")
plt.plot(predicted, label="Predicted")
plt.legend()
plt.show()

Visual Workflow

Stock Data

Yahoo Finance

Preprocess

Scaling
Sequence formation

LSTM Model

Time series forecasting

Prediction

Visualize prices

Forecast

Next-day price

Bonus — Save Model

model.save("stock_lstm_model.h5")

Real-Life Analogy

Stock Prediction = Studying Market Patterns

Just like an investor studies past patterns to predict the next move, an LSTM model studies historical stock prices to predict future trends.

Real-World Applications

Stock Market

Trading bots
Investment strategies

Banking

Loan & risk analysis

Retail

Demand prediction

Weather

Climate forecasting

Energy

Power consumption

Cybersecurity

Anomaly detection

Advantages of LSTM for Stock Prediction

Captures long-term patterns.
Works on sequential data.
Solves vanishing gradient issue.
Excellent for forecasting.
Used in modern financial AI systems.

Disadvantages

Limitation 1 Cannot predict major market events.

Limitation 2 Requires large training data.

Limitation 3 May overfit on noisy data.

Limitation 4 Stock market is influenced by external factors.

Common Mistakes to Avoid

Mistake 1 Not scaling the data.

Mistake 2 Using too short sequences.

Mistake 3 Skipping dropout.

Mistake 4 Overfitting on small datasets.

Best Practices

Quick Tips

Use stock data of 5+ years.
Combine LSTM with technical indicators.
Use Dropout layers.
Always visualize predictions.
Use multiple evaluation metrics.
Retrain model with new data.

Suggested Project Folder Structure

stock_prediction_project/
│
├── data/
│   └── aapl.csv
├── notebooks/
│   └── stock_lstm.ipynb
├── stock_lstm_model.h5
└── README.md

Importance of Stock Price Prediction

Financial AI

Used in trading bots

Career Boost

High demand in fintech

Real-World Impact

Investors, banks, hedge funds

Future-Ready Skill

Part of AI revolution

Golden Rule

REMEMBER

Data + LSTM + Time Patterns = Stock Prediction

Key Takeaway

The Stock Price Prediction Project demonstrates the power of Deep Learning in financial forecasting. Using LSTM and Time Series Analysis, we can predict the next day's stock prices with surprising accuracy — opening doors for trading bots, investment platforms, and financial AI systems.