This project was completed as part of my CodeAlpha Data Science Internship. It uses Machine Learning techniques to predict car prices based on various features such as year, fuel type, transmission, kilometers driven, and owner details.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
The project uses a car price dataset in CSV format for training and evaluating the machine learning model.
- Import Libraries
- Load Dataset
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Train-Test Split
- Model Training
- Model Evaluation
- Price Prediction
- Dataset Preview
- Feature Analysis
- Model Performance
- Data Preprocessing
- Exploratory Data Analysis
- Regression Modeling
- Machine Learning Workflow
- Model Evaluation
Ume Rubab
BS Information Technology Student
Aspiring Data Scientist & Python Developer
🔗 LinkedIn: https://www.linkedin.com/in/umerubab01