https://github.com/muthukumar0908/cardekho_used_car_price_prediction
The project aim is to build a machine learning model that offers users to find current valuations for used cars.
https://github.com/muthukumar0908/cardekho_used_car_price_prediction
data-analysis data-visualization datacleaning eda machine-learning python streamlit
Last synced: 29 days ago
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The project aim is to build a machine learning model that offers users to find current valuations for used cars.
- Host: GitHub
- URL: https://github.com/muthukumar0908/cardekho_used_car_price_prediction
- Owner: Muthukumar0908
- Created: 2024-02-22T16:04:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-11T16:33:27.000Z (about 1 year ago)
- Last Synced: 2025-02-05T12:35:10.451Z (3 months ago)
- Topics: data-analysis, data-visualization, datacleaning, eda, machine-learning, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 1.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# CarDekho_Used_Car_Price_Prediction
Technologies: Data Cleaning, Exploratory Data Analysis (EDA), Visualization and Machine Learning
Domain: Automobile
Problem Statement:
The primary objective of is project is to create a data science solution for predicting used car prices accurately by analyzing a diverse dataset including car model, no. of owners, age, mileage, fuel type, kilometers driven, features and location. The aim is to build a machine learning model that offers users to find current valuations for used cars.
Data Understanding
The Dataset contains multiple excel files, each represents its city, columns in each excel gives you an overview of each car, its details, specification and available features.
Data Collected From: https://www.cardekho.com/usedCars
Dataset Link: https://drive.google.com/drive/folders/16U7OH7URsCW0rf91cwyDqEgd9UoeZAJh
Feature Description Link: https://docs.google.com/document/d/1hxW7IvCX5806H0IsG2Zg9WnVIpr2ZPueB4AElMTokGs/edit
youtube: https://youtu.be/KdhGAjJhpTo
Approach:
Import data from all excel files
Examine the structure of each dataset component (New Car Detail, New Car Overview, etc.).
Check for missing values, outliers, data types and other statistical inference.
Data Preprocessing:
Handle Missing Values: Impute or remove missing values appropriately.
Feature Engineering: Extract relevant information from features like age, mileage, and others.
Encode categorical variables using suitable techniques.
Normalization/Scaling: Scale numerical features to bring them to a comparable range.
Exploratory Data Analysis: Create visualizations to understand the distribution of target variables (used car prices) and relationships between features.
Choose regression models suitable for predicting continuous values
Model Evaluation: Use suitable metrics
Fine-tune Hyperparameters: Optimize model hyperparameters to improve performance.
Feature Importance: Analyze feature importance to understand which features contribute most to the predictions.