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.