A machine learning-based system for predicting prices of commodities/products using historical data and statistical analysis. This project is built to support better decision-making in domains like retail pricing, stock forecasting, and real estate valuation.

• 📂 PDF-based data loading using Langchain and PyPDFLoader
• 📊 Data inspection and preprocessing modules (missing values, summary statistics)
• 🔍 Univariate and multivariate analysis
• 🤖 Machine Learning models for regression-based price prediction
• 📈 Visual insights and performance evaluation

prices-predictorSystem/
│
├── analysis/
│ ├── analyze_src/
│ │ ├── basic_data_inspection.py
│ │ ├── missing_values_analysis.py
│ │ └── univariate_analysis.py
│
├── data/
│ └── sample_data.pdf
│
├── models/
│ └── model_training.py
│
├── notebooks/
│ └── price_prediction_workflow.ipynb
│
├── utils/
│ └── helpers.py
│
├── requirements.txt
└── README.md

1. Clone the repository:

git clone https://github.com/sonalee88/prices-predictorSystem.git
cd prices-predictorSystem

2. Create and activate a virtual environment (optional but recommended):

conda create -n price-predictor python=3.10
conda activate price-predictor

3. Install required dependencies:

pip install -r requirements.txt

Place your PDF datasets inside the /data/ folder.

Use the notebooks/price_prediction_workflow.ipynb to:

Load and inspect data

Clean and prepare datasets

Train machine learning models

Predict future prices and visualize results

Customize model parameters or add new algorithms in models/model_training.py.

Predicting product pricing in e-commerce

Estimating housing prices from real estate data

Forecasting seasonal commodity prices

langchain

pypdf

pandas, numpy

scikit-learn

matplotlib, seaborn

jupyter

Pull requests and forks are welcome! For major changes, please open an issue first to discuss what you'd like to change.

Sonali Kumari

This project is licensed under the MIT License - see the LICENSE file for details.

Let me know if you'd like this customized for deployment (Docker/Streamlit/Flask), or if you're submitting this for an internship, I can tailor it to match the JD too.