Stock Market Prediction:

This project aims to forecast stock market prices using time series analysis techniques and machine learning models, including ARIMA, Prophet, and LSTM. The goal is to help investors and analysts make informed decisions based on data-driven insights.

Table of Contents:

1.Problem Statement
2.Objectives
3.Dataset
4.Technologies Used
5.Project Workflow
6.Modeling Approaches
7.Results
8.Team Members

1.Problem Statement:

        Stock price prediction is a challenging task due to the volatile and non-linear nature of financial markets. This project addresses that challenge by applying AI-driven forecasting models to historical stock data to generate accurate and interpretable predictions.

2.Objectives :

Predict future stock prices using historical time series data

Compare ARIMA, Prophet, and LSTM models

Visualize model outputs and residuals

Deliver insights through performance metrics and visualizations

3.Dataset :

Source: Yahoo Finance (via yfinance), Alpha Vantage, Kaggle

Type: Time-series data (OHLCV format)

Target: Closing stock price

4.Technologies Used :

Languages: Python

Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels, keras, prophet, yfinance

Platform: Google Colab / Jupyter Notebook

Deployment (optional): Streamlit

5.Project Workflow :

1. Data Collection
2. Data Preprocessing
3. Exploratory Data Analysis (EDA)
4. Feature Engineering
5. Model Building (ARIMA, Prophet, LSTM)
6. Evaluation using RMSE, MAE, MAPE
7. Result Visualization
8. (Optional) Web App Deployment

6.Modeling Approaches :

ARIMA: Statistical approach for linear time series forecasting
Prophet: Handles trend and seasonality with flexibility
LSTM: Deep learning model for capturing sequential dependencies

7.Results:

Model performance is evaluated using
RMSE: Root Mean Squared Error
MAE: Mean Absolute Error
MAPE: Mean Absolute Percentage Error
Visualizations include actual vs predicted prices, residual plots, and moving averages.

8.Team Members:

Sriramprasath A – Project Lead
Madhan C – Data Engineer
Sarveesh Kaarthic R – ML Engineer
Mohammed Thoufiq A – Frontend Developer
Abhinanth K K – Data Scientist