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Data Science Project

Description:

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.

Project Goals:

  • 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.

Dataset Preview:

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

Description of features of the dataset:

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

Technologies Used:

  • Python
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn
  • Pickle
  • Streamlit