This project creates a complete machine learning + web application where the user can:

✔ Enter a stock symbol (AAPL, TCS, RELIANCE, BTC-USD, etc.)
✔ See historical price charts
✔ Run ML prediction for next 7–30 days
✔ Visualize forecast
✔ Access it via browser (Flask / Streamlit)

This is similar to what apps like TradingView, MarketMojo, Tickertape provide.

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🛠️ Tech Stack

• Python
• yfinance (stock data)
• scikit-learn / Prophet / LSTM (ML prediction)
• Streamlit / Flask (web UI)
• Plotly / Matplotlib (graphs)

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📦 Install Requirements

pip install yfinance pandas numpy scikit-learn matplotlib streamlit prophet plotly

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📁 Project Structure

stock_predictor/
│── app.py
│── model.py
└── requirements.txt

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📊 1. Fetch Historical Data

import yfinance as yf

def get_stock_data(symbol):
    data = yf.download(symbol, period="5y")
    return data

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🔮 2. Simple Prediction (Prophet Model)

from prophet import Prophet
import pandas as pd

def predict_stock(data, days=30):
    df = data.reset_index()[["Date", "Close"]]
    df.columns = ["ds", "y"]

    model = Prophet()
    model.fit(df)

    future = model.make_future_dataframe(periods=days)
    forecast = model.predict(future)

    return forecast

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🌐 3. Streamlit Web App (app.py)

import streamlit as st
import plotly.express as px
from model import get_stock_data, predict_stock

st.title("📈 Stock Prediction Web App")

symbol = st.text_input("Enter Stock Symbol", "AAPL")

if st.button("Predict"):
    data = get_stock_data(symbol)
    st.subheader("Historical Close Prices")
    fig = px.line(data, y="Close")
    st.plotly_chart(fig)

    forecast = predict_stock(data)
    st.subheader("Forecasted Prices")
    fig2 = px.line(forecast, x="ds", y="yhat")
    st.plotly_chart(fig2)

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🚀 Run the App

streamlit run app.py

Open browser:

http://localhost:8501