STOCK MARKET PREDICTION 📈

This project aims to predict stock market trends using Machine Learning techniques, specifically Random Forest Algorithm for prediction and K-Means Clustering for grouping similar stock behaviors.

The goal is to analyze stock data, cluster similar stocks, and provide predictive insights to help understand market movements.

🚀 FEATURES

Data preprocessing and cleaning of stock market datasets

K-Means Clustering to group stocks with similar movement patterns

Random Forest Regression/Classification for predicting future stock price trends

Visualization of clusters and predictions

Easy-to-extend code for experimenting with other ML models

🛠️ TECH STACK

Programming Language: Python

Libraries:

pandas, numpy – data handling

📊 RESULTS

Stocks are clustered into groups showing similar movement patterns.

Random Forest provides predictive insights into stock price direction (uptrend/downtrend).

Visualizations help compare actual vs. predicted stock trends.

matplotlib, seaborn – data visualization

scikit-learn – machine learning (Random Forest, K-Means)

🔮 FUTURE IMPROVEMENTS

Integrate deep learning models (LSTM, GRU) for time-series forecasting

Use real-time stock APIs for live predictions

Improve feature engineering with technical indicators (RSI, MACD, Moving Averages)

Build a web dashboard for interactive stock predictions