Machine learning projects

Implemented different machine learning techniques

Such as: regression tasks, classification tasks, and clustering.

For file fatih_usta_1 (Prediction of car prices)

In this project, data contains information about different cars and their prices. Objective is to build a model that predicts car prices based on features.

Steps of handling the project:

1) Data Preparation:

    a) Handling the missing values
    b) One hot encoding
    c) Standard scaling

2) Data Exploring & Visualization:

    a) Understanding feature correlations and visualizing them
    b) Calculating correlations

3) Linear Regression to predict prices:

    a) Linear Regression
    b) Polynomial Regression
    c) Lasso Regression
    d) Support Vector Regression with Grid Search to find best parameters

For file fatih_usta_2 (Image Classification)

In this project, data contains information about different images of clothes. Objective is to build a model that predicts clothes based on features.

Steps of handling the project:

1) Data Preparation:

    a) Binary transformation of each clothe. (10 different)

2) Binary Classification:

    a) Logistic Regression
    b) Model evaluation: looking at accuracy, confusion matrix, precision, recall and f1 score.
    c) Plotting of ROC curve, AUC and learning curves.

3) Multiclass Classification:

    a) Support Vector Classification with Grid Search
    b) Decision Trees with Grid Search

For file fatih_usta_3 (Predicting car prices using different machine learning models)

In this project, data contains information about different cars and their prices. Objective is to build a model that predicts car prices based on features.

Steps of handling the project:

1) Data Preparation:

    a) Handling the missing values
    b) One hot encoding
    c) Standard scaling

2) Models:

    a) KNN Regressor
    b) Decision Tree Regressor
    c) Ensemble Bagging Regressor with Grid Search
    d) Random Forest Regressor

3) Feature Importance