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Toyota Vehicle Data Analysis using Machine Learning

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# 🚗 Toyota Vehicle Data Analysis using Machine Learning
This project performs a comprehensive machine learning analysis on a Toyota vehicle dataset by applying both supervised and unsupervised learning algorithms. The objective is to build predictive models, classify vehicle categories, and discover hidden patterns in the data through clustering, supported by robust evaluation metrics and visualizations.
# Live URL-
-- https://toyotodataanalysis.streamlit.app/
# 📌 Project Objectives
-- Preprocess and standardize vehicle data
-- Apply regression, classification, and clustering techniques
-- Compare model performance using appropriate metrics
-- Discover natural groupings in vehicle data
-- Visualize results using dimensionality reduction techniques
-- Provide an interactive Streamlit-based interface
# 🧠 Machine Learning Models Used
🔹 Supervised Learning
  Multiple Linear Regression – to predict continuous values such as price
  K-Nearest Neighbors (KNN) Classifier – for vehicle classification
Support Vector Machine (SVM) Classifier – for robust classification
🔹 Unsupervised Learning
  K-Means Clustering – to group similar vehicles
  Hierarchical (Agglomerative) Clustering – to identify nested cluster structures
# 📊 Evaluation Metrics
🔸 Regression

R² Score

Mean Absolute Error (MAE)

Root Mean Squared Error (RMSE)

🔸 Classification

Accuracy Score

Confusion Matrix

Classification Report (Precision, Recall, F1-score)

🔸 Clustering

Elbow Method (optimal K selection)

Silhouette Score

Davies–Bouldin Index

Calinski–Harabasz Index
# 📈 Visualizations

Actual vs Predicted plots (Regression)

Confusion Matrix (Classification)

Elbow Curve for K-Means

PCA 2D and 3D cluster visualizations

Cluster distribution using value counts
# 🛠️ Technologies Used

Python

Pandas, NumPy

Scikit-learn

Matplotlib / Seaborn

Streamlit

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