Walmart Stock Price Prediction using Machine Learning 🚀 Project Overview

This project aims to predict Walmart’s stock prices between 2000 and 2004 using advanced machine learning techniques. By leveraging historical stock data, technical indicators, and external financial features, the goal is to build a robust regression model capable of forecasting future price movements with high accuracy. 📊 Dataset

Time Period: 2000 - 2004

Data Sources:

    Walmart daily stock prices and volumes

    Market indicators (e.g., NYSE index)

    Commodity prices (gold, silver, brent oil)

    Currency index (USD index)

Preprocessing:

    Feature engineering with rolling statistics and volatility measures

    Time feature encoding using sinusoidal transforms for seasonality

    Missing data handling and normalization

🛠️ Modeling Approach

Exploratory Data Analysis (EDA) to understand patterns and correlations

Feature selection based on statistical and domain knowledge

Use of tree-based ensemble models, especially XGBoost, for regression

Comparison with baseline models (Linear Regression, Random Forest)

Model tuning and cross-validation for optimal performance

📈 Results & Evaluation

Performance metrics: RMSE, MAE, R² scores

Analysis of feature importance highlighting key drivers

Time series visualization comparing actual vs predicted prices

Insights on market behavior and seasonality effects

💡 Key Learnings

Incorporating external financial indicators improves predictive power

Sin-Cos time encoding effectively captures seasonality in stock prices

Feature engineering is critical to boost model accuracy in financial data

XGBoost shows strong performance in regression tasks on stock data

🛠️ Technologies & Tools

Python (Pandas, NumPy, Scikit-learn, XGBoost,LSTM,RNN,GRU)

Data visualization with Matplotlib and Seaborn

Jupyter notebooks for interactive analysis and modeling