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Implementing and Comparing Regression Models Using Scikit-Learn & PyCaret
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youzaina001/ML_supervised
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# Predictive Regression Model using Scikit-Learn and PyCaret ## Project Overview This project develops a predictive regression model to estimate the "Selling_Price" based on different factors. We leverage the power of scikit-learn and PyCaret, two of the most popular machine learning libraries in Python, to preprocess data, select features, train models, and evaluate performance. ### Objectives - To preprocess and analyze the dataset for regression analysis. - To select the most significant features affecting the "Selling_Price". - To compare multiple regression models and select the best performer based on different metrics (MSE, RMSE, etc). - To fine-tune the model parameters for optimal performance. - etc. ## Dataset The dataset used for our regression models is public and can be found in the Kaggle datasets section (https://www.kaggle.com/datasets/nehalbirla/vehicle-dataset-from-cardekho). ### Features - **Age**: [Age of the vehicule] - **Present_Price**: [Present price for the vehicule] - **Kms_Driven**: [Kilometers driven using the car] - **Fuel_Type**: [Fuel type : Diesel or Petrol or CNG] - **Seller_Type**: [Seller type : Dealer or individual] - **Transmission**: [Transmission : Manual or Automatic] - **Owner**: [Owner of the car] ### Target Variable - **[Selling_Price]**: [Description] ## Installation This project is executed using Python 3.11 and the following Python libraries: - NumPy - Pandas - scikit-learn - PyCaret - matplotlib (for data visualization) - seaborn (for data visualization) - etc. To run the jupyter notebook in a container, follow the steps below: - build the docker image using the provided dockerfile by running the command "docker build -t regression ." - run a container using the image you just created by running the command "docker run -p 8888:8888 regression" - to access your virtual environment, use the link obtained via the command line.
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