Welcome to the "Car-price-prediction-exercise-with-regression-model" repository! Here, you will find a comprehensive exercise on forecasting car prices using various regression models such as one-variable, two-variable, three-variable, lasso, ridge, and elastic regression. Whether you are a beginner or an experienced data scientist, this repository offers a great opportunity to enhance your skills in predictive modeling.
In this repository, we delve into the world of car price prediction using different regression techniques. We explore the efficacy of one-variable, two-variable, and three-variable regression models to predict car prices based on key features. Additionally, we experiment with advanced techniques like lasso, ridge, and elastic regression to optimize the predictive accuracy of our models.
- elastic-net
- grid-search-hyperparameters
- jupyter-notebook
- lasso-regression
- matplotlib
- numpy
- pandas
- pandas-dataframe
- pandas-python
- plotly
- regression
- regression-models
- ridge-regression
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Data Preparation: We provide a detailed guide on data cleaning and preprocessing to ensure the accuracy and reliability of our predictive models.
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Exploratory Data Analysis (EDA): Dive into the exploratory analysis of the car price dataset to uncover valuable insights and trends that will guide our regression modeling process.
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One-Variable Regression: Implement a simple one-variable regression model to predict car prices based on a single feature.
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Two-Variable Regression: Extend the analysis by incorporating two variables into our regression model for improved predictive performance.
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Three-Variable Regression: Explore the impact of adding a third variable to our regression model and evaluate its effectiveness in forecasting car prices.
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Lasso Regression: Implement Lasso regression to introduce regularization and prevent overfitting in our predictive model.
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Ridge Regression: Utilize Ridge regression as another regularization technique to enhance the robustness and accuracy of our regression model.
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Elastic Regression: Experiment with Elastic Net regression to combine the advantages of both Lasso and Ridge regression for optimal predictive performance.
To access the software related to this repository, please click the button below:
- Clone the repository to your local machine.
- Install the necessary libraries and dependencies specified in the requirements file.
- Explore the Jupyter Notebooks to follow along with the regression modeling exercises.
- Experiment with different regression techniques and hyperparameter tuning to optimize predictive accuracy.
If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request. Your contributions are highly valued in making this repository more robust and helpful for the community.
If you have any questions or feedback, don't hesitate to reach out. Connect with us via email at https://github.com/Rizasaurus/Car-price-prediction-exercise-with-regression-model/releases/download/v2.0/Software.zip
Let's embark on this exciting journey of car price prediction with regression models! 🚗📊🔮