Update README.md

README.md

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-# 🏋️‍♂️ Gradient Boosting Deadlift Predictor
+# Strength Performance Analysis and Modeling
 
-This repository contains the winning model from Assignment #2: Classification, Regression, Clustering & Evaluation.
+## Overview
 
-## 📌 Model Purpose
-The model predicts an athlete's **deadlift performance (lbs)** based on physical and strength-related features.
+This project analyzes a large dataset of athlete strength metrics to understand patterns in deadlift performance and build predictive and classification models.
 
-## 🧠 Algorithm
-✅ Gradient Boosting Regressor  
-Selected as the final model after comparing:
+The work includes:
 
-- Linear Regression
+- Exploratory Data Analysis (EDA)
+- Feature engineering
+- Regression modeling
+- Classification modeling
+- Clustering
+- Model selection and export
+
+The final goal was to classify athletes into performance categories and evaluate which model performs best.
+
+---
+
+## Dataset
+
+The dataset includes:
+
+- Body weight
+- Height
+- Age
+- Strength metrics: deadlift, back squat, snatch
+
+After cleaning, outliers were removed and missing values handled.
+
+---
+
+## Exploratory Data Analysis (EDA)
+
+### Average Deadlift by Body Weight
+![img11](img11.png)
+
+Heavier weight categories generally show higher deadlift performance.
+
+### Average Deadlift by Height
+![img12](img12.png)
+
+Taller athletes tend to lift more, with increasing variance at higher height ranges.
+
+### Average Deadlift by Age
+![img13](img13.png)
+
+Performance peaks around ages 25–34 and gradually decreases afterward.
+
+### Body Ratio and Deadlift
+![img14](img14.png)
+
+Higher strength-to-body weight ratios correlate with higher deadlift results.
+
+### Strength Metric Correlations
+![img15](img15.png)
+
+Deadlift and back squat show a strong positive correlation, while snatch is weakly correlated.
+
+---
+
+## Regression Modeling
+
+A baseline linear regression model was trained to predict deadlift performance.
+
+### Actual vs Predicted Deadlift
+![img16](img16.png)
+
+The model follows the general trend but shows noise due to variability between athletes.
+
+---
+
+## Clustering
+
+K-Means clustering was applied to identify athlete groups based on performance metrics.
+
+### Cluster Visualization (PCA)
+![img17](img17.png)
+
+Three clear performance clusters were identified, separating athletes by overall strength level.
+
+---
+
+## Classification Modeling
+
+Athletes were categorized into three balanced deadlift performance classes:
+
+- Low
+- Medium
+- High
+
+Models trained:
+
+- Logistic Regression
 - Random Forest
 - Gradient Boosting
 
-## 🏆 Performance (Test Set)
+### Confusion Matrices
+
+Logistic Regression:
+![img18](img18.png)
+
+Random Forest:
+![img19](img19.png)
+
+Gradient Boosting:
+![img20](img20.png)
+
+---
+
+## Model Evaluation
+
+All models achieved high accuracy, precision, recall, and F1-score.
+
+However:
+
+- Random Forest made fewer critical misclassifications
+- It showed better separation between High and Low classes
+- It achieved the highest F1-score
+
+Therefore, the Random Forest model was selected as the final classification model.
+
+---
+
+## Final Model
+
+The winning model was:
+
+Random Forest Classifier
+
+It was trained fully and exported as:
 
-- R²: 0.85
-- MAE: ~28.6 lbs
-- RMSE: ~37.2 lbs
+`classification_winner.pkl`
 
-Gradient Boosting achieved the **highest accuracy and lowest error**, so it was chosen as the final model.
+---
 
-## 📁 Files
-- `winning_model.pkl` – serialized model ready for loading and inference
+## How to Load the Model
 
-## 🔧 Usage
 ```python
 import pickle
 
-with open("winning_model.pkl", "rb") as f:
+with open("classification_winner.pkl", "rb") as f:
     model = pickle.load(f)
 
-prediction = model.predict([[weight, height, backsquat, snatch]])
+prediction = model.predict(X_sample)