This project focuses on predicting the price of diamonds based on various attributes. The goal is to build a machine learning model that accurately estimates the price of a diamond given its features, which can be valuable for both the diamond industry and potential buyers.
- Introduction
- Dataset
- Methodology
- Exploratory Data Analysis
- Feature Engineering
- Modeling
- Results
- Conclusion
- Usage
- Contributing
- License
Diamonds are valued based on several attributes such as carat weight, cut, color, and clarity. This project aims to create a model that predicts diamond prices, allowing sellers and buyers to make informed decisions.
The dataset used for this project is sourced from kaggle. It contains information about various diamond attributes, including carat weight, cut, color, clarity, and dimensions. The dataset was preprocessed to handle outliers, missing values, and categorical variables.
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Data Preprocessing: Cleaning the dataset, handling missing values, and converting categorical variables into numerical representations.
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Exploratory Data Analysis (EDA): Exploring the data to understand distributions, correlations, and potential patterns between diamond features and prices.
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Feature Engineering: Creating new features or transformations that might enhance the predictive power of the model.
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Modeling: Building and training machine learning models to predict diamond prices. Experimenting with regression algorithms such as Linear Regression, Random Forest, and Gradient Boosting.
During the EDA phase, we conducted various analyses, including:
- Distribution of diamond prices.
- Correlations between diamond attributes and prices.
- Visualization of key features' impact on price.
Based on our EDA, we engineered some additional features such as price per carat, which could potentially provide the model with more relevant information.
We used various regression algorithms to train our model on the dataset. The models were evaluated using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) score to assess predictive performance.
Our best-performing model achieved an R-squared score of XX%, indicating its ability to accurately predict diamond prices. The most influential features for price prediction were found to be [list_top_features_here].
Predicting diamond prices is a complex task due to the interplay of multiple factors. This project demonstrates that machine learning models can provide valuable insights into diamond pricing. However, further refinement and fine-tuning are necessary to achieve even more accurate predictions.
To replicate or build upon this project:
- Clone this repository.
- Download the dataset from kaggle and place it in the
datadirectory. - Use Jupyter Notebook or your preferred environment to run the analysis and modeling scripts.
Contributions are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request to this repository.
This project is licensed under the MIT License.