This repository contains the project of the Data Science with Python Career Program from Skill Academy. The project focuses on predicting the selling prices of pre-owned cars using a dataset of car details. It includes exploratory data analysis (EDA), training and evaluating predictive models and deploying a user-friendly web application using Streamlit. Users can input car details on the app, and the predicted selling price will be displayed on the interface.
- Explore and Analyze: Conduct an in-depth exploration of the car dataset to understand its characteristics and identify potential relationships between features.
- Visualize: Create insightful data visualizations to uncover patterns, trends, and anomalies.
- Preprocess and Clean: Address duplicate values, outliers, inconsistencies, and other data quality issues to prepare the data for modeling.
- Machine Learning Model Development: Experiment with various machine learning techniques.
- Model Evaluation: Employ metrics to assess model performance, compare different approaches, and select the best-performing model.
- Sample Prediction: Demonstrate the functionality of the final model by generating predictions on sample data.
- Streamlit Deployment: Develop a user-friendly web application using Streamlit for real-time car price predictions based on user-provided parameters.
A preview of the top five rows of the original or raw dataset.
| name | year | selling_price | kms_driven | fuel | seller_type | transmission | owner | |
|---|---|---|---|---|---|---|---|---|
| 0 | Maruti 800 AC | 2007 | 60000 | 70000 | Petrol | Individual | Manual | First Owner |
| 1 | Maruti Wagon R LXI Minor | 2007 | 135000 | 50000 | Petrol | Individual | Manual | First Owner |
| 2 | Hyundai Verna 1.6 SX | 2012 | 600000 | 100000 | Diesel | Individual | Manual | First Owner |
| 3 | Datsun RediGO T Option | 2017 | 250000 | 46000 | Petrol | Individual | Manual | First Owner |
| 4 | Honda Amaze VX i-DTEC | 2014 | 450000 | 141000 | Diesel | Individual | Manual | Second Owner |
The describing the features of raw dataset, which were shown above, are as follows:
Car_Name: Name of Car sold
Year: Year in which the car was bought from the showroom (means by the car company, not by any seller)
Selling_Price: Price at which car sold
Kms_Driven: Number of Kilometers Car driven before it is sold
Fuel_Type: Type of fuel Car uses
Seller_Type: Type of seller
Transmission: Gear transmission of the car (Automatic / Manual)
Owner: Number of previous owners
- Python
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- Pickle
- Streamlit