Used Car Price Prediction

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Used Car Price Prediction :red_circle: Aim : The aim is to predict the used car price using machine learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/zeeshanlatif/used-car-price-prediction-dataset :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

---

📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

---

:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

---

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Mayureshd-18]

Full name :Mayuresh Dharwadkar GitHub Profile Link :https://github.com/Mayureshd-18 Participant ID (If not, then put NA) :NA Approach for this Project : EDA then model selection and finally the implementation What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)SSOC

@abhisheks008 Pls assign this issue to me.

Regards

[milanprajapati571]

Full name: Milan Prajapati

GitHub Profile Link: GitHub_Profile

Participant ID (If not, then put NA): NA

Approach for this Project:

1. Data Loading - loading the dataset from the provided kaggle link.
2. Exploratory Data Analysis - EDA involves understanding the dataset through statistical summaries and visualizations to gain insights and identify patterns, correlations, and potential issues.
3. Data Processing - This includes handling missing values, encoding categorical variables, feature scaling, and splitting the dataset into training and testing sets.
4. Model Training and Evaluation - We'll train multiple machine learning models and compare their performance. The models we can consider include: Linear Regression Decision Tree Regressor Random Forest Regressor Gradient Boosting Regressor
5. Model Comparision - Evaluate the models using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) to determine the best model.
6. Conclusion and Documentation - Summing up the project by adding the documentation.

What is your participant role? : VSoC

Sir, can You Please assign this project to me...?

[VanshGupta-2404]

Full name : Vansh Gupta GitHub Profile Link : https://github.com/VanshGupta-2404 Participant ID (If not, then put NA) : NA Approach for this Project :

To predict used car prices using machine learning methods, we will follow a structured approach that includes data acquisition, exploratory data analysis (EDA), preprocessing, model building, and evaluation. Here's a step-by-step plan:

1. Data Acquisition First, we'll download the dataset from the provided Kaggle link.

2. Exploratory Data Analysis (EDA)

   • Data Overview: Get basic information about the dataset like number of rows, columns, and data types.
   • Descriptive Statistic: Summarize the central tendency, dispersion, and shape of the dataset’s distribution.
   • Missing Values: Identify and handle missing values.

3. Data Preprocessing

Preprocessing involves cleaning and transforming the raw data into a format suitable for modeling:

• Handling Missing Values: Fill or drop missing values appropriately.
• Encoding Categorical Variables: Convert categorical features into numerical values using techniques like One-Hot Encoding or Label Encoding.
  4. Model Building

We'll build multiple models and compare their performance:

1. Linear RegressionA basic regression model.

2. Random Forest RegressorAn ensemble method that uses multiple decision trees.

3. Gradient Boosting Regressor: Another ensemble method that builds models sequentially.

4. Support Vector Regressor (SVR): Uses support vector machine principles for regression tasks.

   5. Model Evaluation We'll evaluate the models using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. The model with the best performance metrics will be selected as the final model.

5. Code Implementation Here is a Python script that outlines the entire process using popular libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn.

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.): VSOC

[abhisheks008]

Implement 6-7 models for this project/problem statement. Assigned @milanprajapati571