Add Linear Regression Model Trained on Diamond Dataset #35
Description
I would like to contribute a linear regression model that has been trained on the well-known diamond dataset. This model will help predict diamond prices based on various attributes such as carat, cut, color, clarity, and other relevant features
Dataset Overview:
The diamond dataset contains detailed information on the characteristics and prices of diamonds.
Key features include:
- Carat: The weight of the diamond.
- Cut: The quality of the cut (Fair, Good, Very Good, Premium, Ideal).
- Color: Diamond color, with categories ranging from D (best) to J (worst).
- Clarity: The clarity rating of the diamond.
- Depth: Total depth percentage (a measure of the diamond's proportions).
- Table: Width of the top of the diamond relative to the widest point.
- Price: The price of the diamond.
Approach:
Data Preprocessing:
- Clean and preprocess the dataset
- Handle missing values
- Encode categorical variables using One hot encoding and Ordinal encoding
- Find the correlation between features
- Drop duplicate rows
- Normalize numerical features
Model Training:
- Develop a linear regression model using appropriate libraries (e.g., scikit-learn).
- Train the model on a subset of the dataset and validate its performance on a separate test set.
Performance Evaluation:
Evaluate the model using metrics such as
Mean Absolute Error (MAE),
Mean Squared Error (MSE), and
R-squared to ensure its accuracy and reliability.