Main.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

data = {
“Day”: [1, 2, 3, 4, 5, 6, 7],
“Price”: [100, 102, 101, 105, 107, 110, 115]
}

df = pd.DataFrame(data)

X = df[[“Day”]]
y = df[“Price”]

model = LinearRegression()
model.fit(X, y)

future_days = np.array([[8], [9], [10]])
predictions = model.predict(future_days)

print(“Predicted Prices:”, predictions)

plt.scatter(X, y, color=“blue”)
plt.plot(X, model.predict(X), color=“red”)
plt.scatter(future_days, predictions, color=“green”)
plt.xlabel(“Day”)
plt.ylabel(“Price”)
plt.title(“Stock Price Prediction”)
Plt.show()

requirements.txt

pandas
numpy
matplotlib
scikit-learn

README.md

📈 Share Market Prediction (Python)

🔹 Project Overview

This project predicts stock prices using Machine Learning (Linear Regression).

🔹 Features
• Predict future stock prices
• Simple and beginner-friendly model
• Graph visualization using matplotlib

🔹 Technologies Used
• Python
• pandas
• numpy
• matplotlib
• scikit-learn

🔹 How to Run
1. Install libraries:
pip install -r requirements.txt
2. Run the code:
python main.py

🔹 Output
• Predicted stock prices
• Graph showing past + future trend

🔹 Future Improvements
• Use real stock data (API)
• Use advanced ML models
• Add accuracy metrics

🔹 Author

Sakshi Jain