📌 Machine Learning Project 📖 Overview
This repository contains a machine learning model that predicts the price of a product/asset (e.g., Gold, Cars, Houses, etc.) based on various influential features. The goal of this project is to build a reliable regression model that can estimate price values using historical or real-world data.
🧠 Problem Statement
Develop a supervised learning model that:
Learns the relationship between features and price
Predicts future prices accurately
Helps in decision-making or market insights
🚀 Features
Data Preprocessing (cleaning, encoding, scaling)
Exploratory Data Analysis (EDA)
Feature Selection / Engineering
Multiple ML Models evaluated
Model Performance Metrics reported
Visualization dashboards/plots included
Deployment ready code structure
📂 Repository Structure 📁 project-name/ │── 📄 README.md │── 📊 dataset/ │── 📓 notebooks/ │── 📁 src/ │── 📁 models/ │── requirements.txt │── app.py / streamlit_app.py (if deployed)
🔧 Tech Stack
Category Tools
Language Python
Core Libraries NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn
Optional Streamlit / Flask / XGBoost
📈 Model Training & Evaluation
Models tried:
Logistic Regression
Lasso Regression
Support Vector Machine
Random Forest Regressor/Classifier
XGBoost
Evaluation Metrics:
MSE
RMSE
R² Score
Results:
Model Best Score
Random Forest - R2 Score 0.989
Logistic Regression - MSE 0.93
Support Vector Machine - Accuracy score 0.85
📸 Visualizations Included
Heatmap for correlation
Feature importance
Predicted vs Actual results
Data distribution plots
⚙️ How to Run
git clone https://github.com/your-username/your-repo.git
cd your-repo
pip install -r requirements.txt
jupyter notebook
or
python app.py
📬 Contact
For queries or collaboration:
👤 Prince Kumar
🔗 GitHub: https://github.com/your-username