Supply Chain Data Processing & Predictive Analysis

This project focuses on handling supply chain data, inserting it into MongoDB, and performing predictive analysis using machine learning techniques in the Databricks environment.

📌 Project Components

1. Data Insertion (insert_data_mongo.ipynb)

   • Reads supply chain data from a CSV file.
   • Inserts the data into MongoDB under the supply_chain_data collection.

2. Predictive Analysis (predictive_analysis_supply_chain.ipynb)

   • Loads supply chain data from MongoDB.
   • Uses Apache Spark in Databricks for data preprocessing.
   • Trains a Random Forest Regressor to predict inventory pricing.
   • Stores predictions in MongoDB under the prediction_data collection.

⚙️ Tech Stack

• MongoDB (for data storage)
• Databricks (Apache Spark environment) (for predictive analysis)
• Python Libraries:
  • pymongo (for MongoDB interaction)
  • pyspark (for machine learning and data processing in Databricks)
  • pandas (for handling CSV data)

🚀 Setup Instructions

1. Install Dependencies (If running locally needs pyspark installation, not needed in Databricks)

   pip install pymongo pyspark pandas

2. Insert Data into MongoDB

   • Run insert_data_mongo.ipynb in Databricks to load supply chain data into MongoDB.

3. Run Predictive Analysis

   • Run predictive_analysis_supply_chain.ipynb in Databricks to train the model and store predictions.

📈 Predictive Analysis Workflow

• Uses Random Forest Regression for price prediction.
• Features: Price and Stock Levels.
• Trained model predicts pricing trends in supply chain management.

📊 Output

• Predictions are stored in MongoDB for further business insights.

⚡ Running in Databricks

• Ensure Databricks Runtime includes Apache Spark.
• Upload and run notebooks in Databricks.
• Connect Databricks to MongoDB for data storage and retrieval.