ML-Models

This repo consists of all my ML-projects.

The list of the projects are below:

1. Credarove

    This is a machine learning model to predict if a credit card application will get approved.
   +-----------------+
   | Steps followed: |
   +-----------------+
    - First, we will start off by loading and viewing the dataset.
    - We will see that the dataset has a mixture of both numerical and non-numerical features, that it contains values from different ranges, plus that it contains a number of missing entries.
    - We will have to preprocess the dataset to ensure the machine learning model we choose can make good predictions.
    - After our data is in good shape, we will do some exploratory data analysis to build our intuitions.
    - Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted.

( Model used: Logistic Regression )

2. Generify

   This is a machine learning model to classify songs as being either 'Hip-Hop' or 'Rock'. 
   +-----------------+
   | Steps followed: |
   +-----------------+
   
   - First, We load the metadata about our tracks alongside the track metrics compiled by The Echo Nest.
   - Then, we perform PCA on our data.
   - After that we train a Decision Tree Classifier to classify the genres.
   - Then we train a Logistic Regression so as to compare which classifier performs better.
   - Now, we balance our data for a greater performance.
   - Finally, we used Cross-Validation to evaluate our models.

( Model used: Decision tree Classifier and Logistic Regression )