Summary
Build Linear Regression, Ridge Regression, Lasso Regression, Elastic-Net algorithms
Performed end to end Data Science steps, Exploratory Data Analysis, Quantitative Data Analysis, Data Cleaning, Feature Engineering, Multicollinearity Check, Detect Outliers, Pre-Processing, Implement Models, Cross-Validation, Feature Importance, Compare Models, Save Model, Predictions
Building model using pandas, numpy, matplotlib, seaborn, sklearn, scipy, cross_validate.
Evaluated models using the following metrics mean_absolute_error, mean_squared_error, r2_score, root mean squared error
The Project
This car price prediction project leverages various regression models to estimate car values based on their features. The project involved implementing Linear Regression, Ridge Regression, Lasso Regression, and Elastic-Net algorithms, covering the full data science pipeline from data processing to model evaluation. Here’s a summary of the key steps:
Data Analysis and Preparation: Completed Exploratory and Quantitative Data Analysis, data cleaning, feature engineering, and checked for multicollinearity and outliers.
Model Building and Implementation: Built models using tools like pandas, numpy, matplotlib, seaborn, sklearn, scipy, and cross-validation.
Evaluation and Comparison: Evaluated model performance using metrics such as mean_absolute_error, mean_squared_error, r2_score, and root mean squared error.
Predictions and Final Model: Saved and deployed the final model, generating predictions for car prices based on input features.
This project highlights the power of regression techniques in creating accurate price predictions, offering a valuable tool for estimating car values in the automotive market.
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