us-foods-analysis

Exploratory data analysis and predictive modeling for U.S. Foods distribution and its customer segmentation analysis. Includes data preprocessing, visualization, and insights generation

U.S. Foods Analysis

This repository contains the exploratory data analysis (EDA) and predictive modeling for the U.S. Foods case study. The goal of this project is to analyze provided data, identify patterns, and develop models to generate actionable insights.

Features

• Data Preprocessing: Cleaning and preparation of raw data.
• Exploratory Data Analysis (EDA): Uncovering trends and patterns through visualizations.
• Predictive Modeling: Developing machine learning models to predict key metrics.
• Results and Insights: Summarizing findings and recommendations.

Files

• US_foods_casestudy.ipynb: Main Jupyter Notebook with the analysis and results.
• data/: Contains the input datasets (not included here for confidentiality).
• models/: Includes saved models and performance metrics (if applicable).

Getting Started

Prerequisites

• Python 3.8 or higher
• Required libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Install dependencies using:

pip install -r requirements.txt