https://github.com/pandaiscodingcpu/carcalcx
A linear regression model created using Sklearn, pandas and numpy to predict the car prize using parameters such as Brand, Fuel type, age etc.
https://github.com/pandaiscodingcpu/carcalcx
Last synced: about 1 year ago
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A linear regression model created using Sklearn, pandas and numpy to predict the car prize using parameters such as Brand, Fuel type, age etc.
- Host: GitHub
- URL: https://github.com/pandaiscodingcpu/carcalcx
- Owner: pandaiscodingcpu
- Created: 2025-02-25T11:54:31.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-15T14:31:14.000Z (about 1 year ago)
- Last Synced: 2025-03-15T15:29:16.669Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 388 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# CarCalcX
A linear regression model created using Sklearn, pandas and numpy to predict the car prize using parameters such as Brand, Fuel type, age etc.
# Libraries used and Workflow
pandas: For data analysis , used https://www.kaggle.com/datasets/taeefnajib/used-car-price-prediction-dataset from kaggle
Scikit-Learn: To train a linear regression model
Numpy: To use log transformation on large price values in the dataset
Pickle (with AI): To save the model
Streamlit: Used Streamlit to deploy the model but due to some technical issues the model has not been completely deployed.
# Additional changes
Used feature engineering to reduce RMSE,MSE,R(square) on the model
📊 Model Evaluation AFTER Feature Engineering:
MAE : 0.32
MSE : 0.19
RMSE : 0.44
R² Score : 0.7306
# STEPS TO USE THE MODEL
STEP 1: Download the dataset
STEP 2: Download all the reuqired libraries
STEP 3: Run the two files 1. data_gathering.ipynb and 2. final_dataset.ipynb in jupyter notebook
STEP 4: using app.py type the command in terminal: streamlit run app.py
STEP 5: You will see the web interface follow further commnands to use the model.