Project Walkthrough Video [Click here to watch the video](https://youtu.be/EJxIwzKkG3g) # 0. Project Overview & Initial Workflow ## **0.1 Project Goal** The main objective of this project was to analyze the **diamonds dataset** and understand: - What drives the **price** of a diamond, - How **physical attributes** (carat, x/y/z dimensions) behave, - How **categorical qualities** (cut, color, clarity) influence pricing, - And which features are the most important for predictive modeling. This includes **EDA, feature engineering, dimensionality reduction, clustering, and model interpretation**. --- ## **0.2 Initial Setup & Data Loading** Before analysis, we: 1. Loaded the dataset (`diamonds.csv`) 2. Inspected its structure 3. Checked for missing values 4. Validated data types 5. Identified potential data issues such as: - Zero or unrealistic values (especially in x, y, z) - Extreme outliers in price and carat --- ## **0.3 Cleaning & Preprocessing Steps** We performed the following: - Removed or corrected invalid dimension values (e.g., x/y/z = 0) - Detected extreme outliers using boxplots - Standardized numeric distributions when needed - Encoded categorical variables - Created engineered features: - `volume = x * y * z` - `price_per_carat = price / carat` - PCA components (`pca1`, `pca2`) - Ratios (xy_ratio, yz_ratio, xz_ratio) --- ## **0.4 Core Analytical Questions** Throughout the project, we focused on several central questions: ### **1. Distribution & Quality of the Data** - What do the numeric features look like? - Are there extreme outliers or incorrect values? ### **2. Relationship Between Size & Price** - How strongly does carat determine price? - Do heavier diamonds increase in price linearly or exponentially? ### **3. Influence of Categorical Features** - How do cut, color, and clarity affect pricing? - Are some categories overpriced or underpriced? ### **4. Multicollinearity & Feature Dependencies** - Are x, y, z essentially duplicating carat? - What is the correlation structure? ### **5. Machine Learning Insights** - Which features matter most for predict # Diamonds Dataset — Full EDA, Visualizations & Feature Analysis This project explores the **diamonds dataset** using a full workflow: - Exploratory Data Analysis (EDA) - Outlier detection - Distribution analysis - Relationship analysis (carat–price, dimensions–price) - Correlation heatmaps - Clustering (PCA + KMeans) - Feature importance (Linear Regression + Random Forest) All generated graphics are stored and displayed inside this repository. --- # 1. Exploratory Data Analysis (EDA) ## 1.1 Boxplots — Outlier Detection ![Boxplots Outliers](boxplots_outliers.png.png) ### **What we looked for** - Detect extreme outliers in: **carat, depth, table, x, y, z, price** - Identify values that may distort models ### **What we found** - Very strong outliers in **price** (long right tail) - Outliers in **carat** and the 3D dimensions **x/y/z** - depth & table are stable with minimal extreme values --- ## 1.2 Boxplots — Numeric Feature Comparison ![Boxplots Numeric Features](boxplots_numeric_features.png.png) ### **What we looked for** - Check consistency between numeric fields - Identify skewness and unusual patterns ### **What we found** - Most numeric columns are **right-skewed** - carat, price, x/y/z show wide variance - Indicates the need for normalization before modeling --- ## 1.3 Distribution Plots — Numeric Features ![Distributions Numeric](distributions_numeric_features.png.png) ### **What we looked for** - Understand the shape of each numeric feature - Detect multimodality or measurement clusters ### **What we found** - price = heavily right-skewed - carat = concentrated around 0.3–1.5 - x/y/z = show clear clustered peaks → indicates standard diamond sizes in the market --- ## 1.4 Distribution (Histograms + KDE) ![Distribution Histograms](distribution_histograms.png.png) ### **What we looked for** - Compare distribution curves to understand typical ranges - Check for measurement errors ### **What we found** - x/y/z show repeating peaks → indicates standard diamond cut proportions - price again shows extreme right-tail - No obvious errors except a few odd large values --- # 2. Relationship Exploration ## 2.1 Carat vs Price — Colored by Cut ![Carat by Cut](carat_vs_price_colored_by_cut.png.png) ### **What we looked for** - Understand how **carat weight** drives pricing - Check if **cut quality** influences pricing ### **What we found** - Strong positive correlation: **higher carat → higher price** - High-cut diamonds (Ideal, Premium) form slightly higher price clusters - Clear size bands (0.5, 1.0, 1.5 carat) visible --- ## 2.2 Carat vs Price — Hexbin Density ![Hexbin Carat Price](carat_vs_price_hexbin.png.png) ### **What we looked for** - Density concentration of the dataset - Identify the most common market prices ### **What we found** - Dense cluster around **0.2–1.2 carat** and **$500–$6000** - Very few large stones (3–5 carat) --- ## 2.3 ### Average Price by Cut, Color, and Clarity ![Overlay Density]( ![avg_price_by_cut_color_clarity](https://cdn-uploads.huggingface.co/production/uploads/6911ea1a4cdaf097dd0456d9/pzu7dPTsyqI1oBl6XoDbs.png) Key insights: - **Cut:** Premium diamonds have the highest average price, while Ideal tends to be lower. - **Color:** Colors I and J show higher average prices compared to others. - **Clarity:** SI2 appears with the highest average price, likely influenced by larger carat values within this group. --- ## 2.4 Diamonds Dimensions vs Price ![Diamonds Size vs Price](diamonds_size_vs_price.png.png) ### **What we looked for** - Relationship between x, y, z dimensions and price ### **What we found** - Strong increasing trend for all dimensions - x and y show the clearest linear relationship - Some unrealistic dimension values detected → outliers --- # 3. Statistical Relationships ## 3.1 Correlation Matrix (Numeric) ![Correlation Matrix](correlation_matrix_numeric.png.png) ### **What we looked for** - Identify multicollinearity - See which features are most correlated with price ### **What we found** - carat has the strongest correlation with price (0.92) - x, y, z strongly correlated with each other (>0.98) - depth & table weakly correlated --- ## 3.2 Heatmap — Key Numeric Features ![Heatmap](heatmap_numeric_features.png.png) ### **What we found** - Confirms carat is the #1 price driver - Dimensions act as proxies for carat - Confirms multicollinearity → PCA or feature reduction required --- # 4. Advanced Visualizations ## 4.1 KMeans Clusters (PCA Visualization) ![KMeans PCA](kmeans_clusters_pca_visualization.png.png) ### **What we looked for** - Whether diamonds naturally form clusters - If PCA can separate quality groups ### **What we found** - The dataset forms clear clusters after PCA - Cluster 0 is dominant (majority of market) - Smaller clusters show unique structural patterns --- ## 4.2 Pairplot — Key Features ![Pairplot](pairplot_key_features.png.png) ### **What we looked for** - Examine all pairwise relationships visually ### **What we found** - Strong linear relationships among x, y, z - Clear nonlinear trend for carat–price - Strong clustering by typical diamond sizes --- # 5. Feature Importance ## 5.1 Linear Regression — Coefficients ![LR Feature Importance](feature_importance_linear_regression.png.png) ### **What we looked for** - Identify which features increase or decrease price ### **What we found** - carat has the highest positive effect - Higher clarity grades reduce price impact (negative coefficients for lower clarity) - color J decreases price significantly --- ## 5.2 Random Forest — Top 20 Features ![RF Importance](rf_feature_importance.png.png) ### **What we looked for** - Nonlinear feature importance ranking - Impact of engineered features ### **What we found** - **price_per_carat** = most powerful feature - volume & PCA components ranked very high - confirms importance of dimensional/size attributes - categorical dummy variables are much weaker ## Regression Model Comparison We evaluated three regression models: - **Linear Regression** (baseline) - **Random Forest Regressor** - **Gradient Boosting Regressor** ### Results | Model | MAE | RMSE | R² | |--------------------|----------|----------|-----------| | Linear Regression | 0.0840 | 0.1248 | 0.9841 | | Random Forest | **0.0042** | **0.0194** | **0.9996** | | Gradient Boosting | 0.0250 | 0.0442 | 0.9980 | ### Summary - **Random Forest** achieved the best performance: lowest errors and highest R². - **Gradient Boosting** also performed very well but was slightly weaker. - **Linear Regression** performed significantly worse, showing the price relationships are **non-linear**. ### Conclusion **Random Forest is the best regression model** and was selected as the final model for this part. Part 7: Regression → Classification ### Creating Price Classes (Quantile Binning) To convert the continuous *price* variable into a classification target, we split the data into **three equal-sized groups (33% each)** using quantiles. - **Class 0** → bottom 33% (cheapest diamonds) - **Class 1** → middle 33% - **Class 2** → top 33% (most expensive diamonds) This ensures balanced class sizes and prevents bias toward any group. ### Class Balance Check After creating the three price classes, we checked whether the classes were balanced. **Training Set:** Each class contains ~33% of the samples **Test Set:** Same distribution — ~33% per class This confirms the split is balanced and suitable for training classification models. ## 8.1 Conceptual Questions ### What is more important in our task — Precision or Recall? In our diamond price classification task, **precision is slightly more important**. We want to avoid labeling a low-value diamond as an expensive one (false positives). This is especially relevant because misclassifying prices upward is more harmful than misclassifying downward. ### What is more critical — False Positive or False Negative? **False Positives are more critical.** A False Positive here means: The model predicts “expensive” when the diamond actually belongs to a cheaper class. This type of error is more problematic in real-world scenarios (e.g., pricing, valuation, inventory management), because it **overestimates** the diamond’s value and can lead to financial loss. ## Part 8.3 — Classification Models Evaluation After converting the regression problem into a 3-class classification task, we trained and evaluated three models: - Logistic Regression - Random Forest - Gradient Boosting ### What we evaluated For each model we generated: - **Classification Report** (precision, recall, f1-score, support) - **Confusion Matrix** (to understand where the model makes mistakes) --- ## Results Summary All three models performed extremely well, with **Gradient Boosting** achieving the best scores: | Model | Accuracy | F1-macro | |--------------------|----------|----------| | Logistic Regression | 0.9866 | 0.9866 | | Random Forest | 0.9926 | 0.9926 | | Gradient Boosting | 0.9933 | 0.9933 | ### Key Insights - All models classify the three price classes with **very high precision and recall**. - **Gradient Boosting** makes the fewest mistakes across classes. - Misclassifications mostly occur between **adjacent price classes** (e.g., class 1 ↔ class 2), which is expected because diamond prices are continuous. - No model suffers from class imbalance problems. --- ## Confusion Matrices & Reports ### Logistic Regression ( ![image](https://cdn-uploads.huggingface.co/production/uploads/6911ea1a4cdaf097dd0456d9/6TfUlqSiD7rd4XKVFghRQ.png) ) ### Random Forest ( ![image](https://cdn-uploads.huggingface.co/production/uploads/6911ea1a4cdaf097dd0456d9/JGM6eXa09Tz1NlE0ydVOV.png) ) ### Gradient Boosting ( ![image](https://cdn-uploads.huggingface.co/production/uploads/6911ea1a4cdaf097dd0456d9/Q0Svg6_AlVwLYlZEkeFvQ.png) ) --- ## Winner **Gradient Boosting** is the best performing model → Highest Accuracy → Highest F1-macro → Most stable confusion matrix This is the model selected for export and deployment.