README.md

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.