Final project

• Cars Classification using Machine Learning

1. Introduction: This documentation outlines the key steps and decisions involved in building a machine learning model for classifying cars into different categories. The dataset 'cars_class.csv' contains information about various cars, including numerical features and the target variable representing the class of the car.

2. Dataset Details:

   • Dataset File: 'cars_class.csv'
   • Multi-class Classification: The goal is to classify cars into one of the four classes: 0 (bus), 1 (Opel Manta), 2 (Saab), or 3 (Van).
   • Dataset Size: The dataset consists of 719 samples.
   • Numerical Features: There are 18 numerical features representing different attributes of the cars.

3. Data Preparation:

   • Load Data: Load the 'cars_class.csv' dataset into your code.
   • Splitting: Split the dataset into training and testing sets, keeping 20% of the data aside as the test set for evaluation.

4. Preprocessing Techniques:

   • Data Cleaning: Check for missing values and handle them appropriately.
   • Feature Scaling: Normalize or standardize the numerical features to ensure all features are on a similar scale.
   • Data Transformation: Perform any necessary data transformations such as log transformations, handling skewed distributions, or encoding categorical variables (if present).

5. Machine Learning Techniques: Apply different machine learning algorithms for car classification. Some commonly used algorithms for multi-class classification include:

   • Logistic Regression
   • Support Vector Machines (SVM)
   • Random Forest
   • Gradient Boosting Methods (e.g., XGBoost, LightGBM)
   • Neural Networks (e.g., Multi-layer Perceptron)

6. Hyperparameter Tuning: Optimize the performance of the machine learning models by tuning their hyperparameters. Use techniques like grid search, random search, or Bayesian optimization to find the best combination of hyperparameters for each algorithm.

7. Model Building: Build a final model, named 'final_model,' using the optimized hyperparameters and the chosen machine learning algorithm.

8. Model Evaluation:

   • Accuracy: Calculate the accuracy of the 'final_model' on the test data to measure the overall correctness of the predictions.
   • F1-Score: Compute the F1-score to evaluate the model's performance in terms of precision and recall.
   • Confusion Matrix: Display the confusion matrix to visualize the model's performance across different classes.

9. Feature Importance: Assess the importance of different features in the 'final_model.' Use techniques like feature importance plots, permutation importance, or SHAP values to understand the contribution of each feature towards the model's predictions.

• Car Price Prediction using Machine Learning

1. Introduction: This documentation outlines the key steps and decisions involved in building a machine learning model for predicting car prices. The dataset 'cars_price.csv' contains information about various cars, including 25 features and the target variable representing the price of the car.

2. Dataset Details:

   • Dataset File: 'cars_price.csv'
   • Regression Problem: The goal is to predict the price of the car, which is a continuous numerical value.
   • Dataset Size: The dataset consists of 206 samples.
   • Features: There are 25 features representing different attributes of the cars.

3. Data Preparation:

   • Load Data: Load the 'cars_price.csv' dataset into your code.
   • Splitting: Split the dataset into training and testing sets, keeping 20% of the data aside as the test set for evaluation.

4. Preprocessing Techniques:

   • Data Cleaning: Check for missing values and handle them appropriately.
   • Feature Scaling: Normalize or standardize the numerical features to ensure all features are on a similar scale.
   • Data Transformation: Perform any necessary data transformations such as log transformations, handling skewed distributions, or encoding categorical variables (if present).

5. Machine Learning Techniques: Apply different machine learning algorithms for car price prediction. Some commonly used algorithms for regression include:

   • Linear Regression
   • Decision Trees
   • Random Forest
   • Gradient Boosting Methods (e.g., XGBoost, LightGBM)
   • Support Vector Regression (SVR)
   • Neural Networks (e.g., Multi-layer Perceptron)

6. Hyperparameter Tuning: Optimize the performance of the machine learning models by tuning their hyperparameters. Use techniques like grid search, random search, or Bayesian optimization to find the best combination of hyperparameters for each algorithm.

7. Model Building: Build a final model, named 'final_model,' using the optimized hyperparameters and the chosen machine learning algorithm.

8. Model Evaluation:

   • Mean Squared Error (MSE): Calculate the MSE of the 'final_model' on the test data to measure the average squared difference between the predicted and actual car prices.
   • Mean Absolute Error (MAE): Compute the MAE to evaluate the model's performance in terms of the average absolute difference between the predicted and actual car prices.
   • R2-Score: Calculate the R2-score to measure the proportion of the variance in the target variable that is predictable by the model.

9. Feature Importance: Assess the importance of different features in the 'final_model.' Use techniques like feature importance plots or permutation importance to understand the contribution of each feature towards predicting car prices.