Used Car Price Prediction
using Machine Learning Models & Techniques.
In this data science project, I have conducted a comprehensive Exploratory Data Analysis (EDA) on a dataset containing information about used cars. The dataset encompasses a variety of features such as the car's make, model, year of manufacture, mileage, fuel type, and various other factors that influence the pricing of used cars. The primary objective of this project is to gain valuable insights into the dataset through EDA and build a robust machine learning model to predict the price of used cars.
| Make | Model | Year | Mileage | Fuel Type | Price |
|---|---|---|---|---|---|
| Toyota | Camry | 2018 | 50,000 | Petrol | $20,000 |
Live Demo Of results prediction deployed on streamlit.
Project 1: Car Price Prediction using ML models
resume
Data Science and Machine Learning Steps:
- Data Collection: Gathered a dataset with information on used cars.
- Exploratory Data Analysis (EDA): Explored and analyzed the dataset to understand patterns and relationships.
- Data Preprocessing: Cleaned and transformed the data to prepare it for machine learning models.
- Feature Engineering: Created new features or modified existing ones to improve model performance.
- Model Selection: Chose suitable machine learning models for the prediction task.
- Model Training: Trained the selected models on the training dataset.
- Model Evaluation: Assessed the performance of the trained models using validation data.
- Hyperparameter Tuning: Optimized model parameters for better predictions.
- Deployment: Deployed the final model for real-world predictions.