Jane Street Market Prediction

This repository contains a data analysis project for the Jane Street Market Prediction Kaggle competition. The goal of this competition is to create a model that predicts whether to accept or reject a trade.

Project Overview

The project is structured in three Jupyter notebooks that cover the same analysis:

• janes-stock.ipynb: Main notebook with the complete analysis.
• Clustering+PCA: Notebook focused on clustering and PCA.
• feature+pca: Notebook focused on feature engineering and PCA.

The analysis includes the following steps:

1. Data Loading and Optimization: The training data is loaded from train.csv, and its memory usage is optimized by converting float64 columns to float32.

2. Feature Engineering:

   • Clustering: K-Means clustering is used to group similar features together.
   • PCA: Principal Component Analysis (PCA) is applied to reduce the dimensionality of the feature space.

3. Correlation Analysis: The correlation between the principal components and the target variables (action, weight, and resp) is analyzed.

Getting Started

To run the analysis, you will need to have Python 3 and Jupyter Notebook installed. You will also need to install the following libraries:

• numpy
• pandas
• matplotlib
• scikit-learn

You can install these libraries using pip:

pip install numpy pandas matplotlib scikit-learn

Once you have installed the dependencies, you can run the Jupyter notebooks in this repository.