📈 Stock Price Prediction Web App

🔮 Goal:
Build a machine learning-based web app that predicts future stock prices 📉📈 using historical data and helps users make informed investment decisions 💹.

Built with Streamlit for an interactive and user-friendly experience. 🧑‍💻📊

🧠 Project Breakdown

📊 1. Exploratory Data Analysis (EDA)

• Visualized historical stock trends
• Analyzed volume, moving averages, and volatility
• Checked seasonality and patterns in prices

🧹 2. Data Preprocessing

• Handled missing values
• Scaled and normalized features
• Converted date-time for trend analysis

🤖 3. Model Training

• Used regression models like:
  • 📈 Linear Regression
  • 🔁 LSTM (Optional - for deep learning enhancement)

🎯 4. Outcome

• A trained model that predicts future stock prices
• Helps support data-driven 📊 investment strategies

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*** 🧠 Features

• 📅 Upload or fetch historical stock data (CSV or via API)
• 🔍 Analyze stock trends with dynamic charts
• 🤖 Predict future prices using:
  • Linear Regression
  • (Optional: Add LSTM or other models later)
• 📊 Visualize prediction results interactively
• ⚡ Simple & clean UI using Streamlit **

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🧠 Features

• 📅 Upload or fetch historical stock data (CSV or via API)
• 🔍 Analyze stock trends with dynamic charts
• 🤖 Predict future prices using:
  • Linear Regression
  • (Optional: Add LSTM or other models later)
• 📊 Visualize prediction results interactively
• ⚡ Simple & clean UI using Streamlit

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🧰 Tech Stack

• Python 🐍
• Pandas, NumPy for data handling
• Matplotlib, Seaborn for visualization
• Scikit-learn for ML models
• Streamlit 🌐 for web UI
• (Optional) yfinance or Alpha Vantage for real-time data

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🚀 How to Run the App

🖥️ 1. Clone the repository

git clone https://github.com/your-username/stock-price-prediction.git
cd stock-price-prediction

📦 2. Install dependencies

bash
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pip install -r requirements.txt


▶️ 3. Run the Streamlit app

bash
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streamlit run app.py


📂 Dataset Info

Format: CSV with columns like Date, Open, High, Low, Close, Volume

Can use:

Your own historical data

Or fetch using APIs like yfinance



🎯 Outcome
A lightweight, fast, and interactive app that:

Predicts next-day or future closing prices

Helps traders, investors, and students understand market patterns

Can be extended for other financial assets or use cases