Time-Series-Stock-Markets

Project Overview:

This project aims to analyze and forecast stock market trends using time series analysis techniques.

Project Objectives :

• Understand time series concepts such as trend, seasonality, and noise.
• Implement models like ARIMA, SARIMA, Prophet, and LSTM for forecasting.
• Visualize insights and predictions through dashboards or reports.
• Evaluate and compare model accuracy
• Collect and preprocess historical stock market data.

Tech Stack & Tools :

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Statsmodels
• Facebook Prophet
• TensorFlow/Keras (for LSTM)
• Streamlit

Create a Python virtual environment (first step):

python -m venv venv

venv\Scripts\activate

Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted -Force

pip install -r requirements.txt

deactivate

resource of learning (second step):

Python

https://www.w3schools.com/python/

Pandas

https://pandas.pydata.org/

NumPy

https://numpy.org/

Scikit-learn

https://matplotlib.org/

Statsmodels

https://www.statsmodels.org/stable/tsa.html

Facebook Prophet

https://facebook.github.io/prophet/

TensorFlow

https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM

Streamlit

https://docs.streamlit.io/